
from IPython.display import IFrame
import datetime
print("Notebook was last executed on:", datetime.date.today().strftime("%Y-%b-%d"), "with Python version")
!python --version

Notebook was last executed on: 2024-Mar-25 with Python version
Python 3.11.5


# Source: Wikipedia
IFrame('https://upload.wikimedia.org/wikipedia/en/2/2f/Shark_Tank_India.jpg', width=330, height=330)


import warnings
warnings.filterwarnings('ignore')

import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)

import matplotlib.pyplot as plt
import seaborn as sns
from babel.numbers import format_currency
from wordcloud import WordCloud, STOPWORDS
import geopandas as gpd
import plotly.express as px
import plotly.io as pio
pio.templates.default = "plotly_dark"
pio.renderers.default = 'notebook'


shark_tank = pd.read_csv('Shark Tank India.csv', encoding = "ISO-8859-1")
 
nRow, nCol = shark_tank.shape
print(f'\nThere are {nRow} rows and {nCol} columns in the dataset')

total 124
-rw-r--r-- 1 nobody nogroup 124427 Mar 16 07:41 'Shark Tank India.csv'

There are 441 rows and 78 columns in the dataset


shark_tank = pd.read_csv('Shark Tank India.csv',encoding = "ISO-8859-1")

nRow, nCol = shark_tank.shape
print(f'\nThere are {nRow} rows and {nCol} columns in the dataset')

There are 320 rows and 64 columns in the dataset


# Word cloud based on episode titles
text = " Shark Tank India ".join(cat for cat in shark_tank.loc[shark_tank['Episode Title'].notnull()]['Episode Title'])
stop_words = list(STOPWORDS) + ["Ka", "Ki", "Ke", "Ko", "Se", "Hai", "Ek"]
wordcloud = WordCloud(width=2000, height=1500, stopwords=stop_words, background_color='white', colormap='Reds', collocations=False, random_state=2024).generate(text)
plt.figure(figsize=(25,20))
plt.imshow(wordcloud)
plt.axis("off")
plt.show()


shark_tank.head(8)


shark_tank.tail(10).T


shark_tank['Season Number'] = shark_tank['Season Number'].astype(pd.Int32Dtype())
shark_tank['Episode Number'] = shark_tank['Episode Number'].astype(pd.Int32Dtype())
shark_tank['Pitch Number'] = shark_tank['Pitch Number'].astype(pd.Int32Dtype())

shark_tank['Number of Presenters'] = shark_tank['Number of Presenters'].astype(pd.Int32Dtype())
shark_tank['Male Presenters'] = shark_tank['Male Presenters'].astype(pd.Int32Dtype())
shark_tank['Female Presenters'] = shark_tank['Female Presenters'].astype(pd.Int32Dtype())
shark_tank['Transgender Presenters'] = shark_tank['Transgender Presenters'].astype(pd.Int32Dtype())
shark_tank['Couple Presenters'] = shark_tank['Couple Presenters'].astype(pd.Int32Dtype())

shark_tank['Gross Margin'] = shark_tank['Gross Margin'].astype(pd.Int32Dtype())
shark_tank['Net Margin'] = shark_tank['Net Margin'].astype(pd.Int32Dtype())

shark_tank['Started in'] = shark_tank['Started in'].astype(pd.Int32Dtype())
shark_tank['Yearly Revenue'] = shark_tank['Yearly Revenue'].astype(pd.Int32Dtype())

shark_tank['Received Offer'] = shark_tank['Received Offer'].astype(pd.Int32Dtype())
shark_tank['Accepted Offer'] = shark_tank['Accepted Offer'].astype(pd.Int32Dtype())


shark_tank.sample(10).style.set_properties(**{"background-color": "#2a9d8f","color":"white","border": "1px solid black", 'font-size': '10pt'})


shark_tank.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 441 entries, 0 to 440
Data columns (total 78 columns):
 #   Column                     Non-Null Count  Dtype  
---  ------                     --------------  -----  
 0   Season Number              441 non-null    Int32  
 1   Startup Name               441 non-null    object 
 2   Episode Number             441 non-null    Int32  
 3   Pitch Number               441 non-null    Int32  
 4   Season Start               441 non-null    object 
 5   Season End                 321 non-null    object 
 6   Original Air Date          410 non-null    object 
 7   Episode Title              441 non-null    object 
 8   Anchor                     441 non-null    object 
 9   Industry                   441 non-null    object 
 10  Business Description       441 non-null    object 
 11  Company Website            430 non-null    object 
 12  Started in                 321 non-null    Int32  
 13  Number of Presenters       441 non-null    Int32  
 14  Male Presenters            381 non-null    Int32  
 15  Female Presenters          206 non-null    Int32  
 16  Transgender Presenters     3 non-null      Int32  
 17  Couple Presenters          437 non-null    Int32  
 18  Pitchers Average Age       441 non-null    object 
 19  Pitchers City              437 non-null    object 
 20  Pitchers State             438 non-null    object 
 21  Yearly Revenue             210 non-null    Int32  
 22  Monthly Sales              210 non-null    float64
 23  Gross Margin               117 non-null    Int32  
 24  Net Margin                 64 non-null     Int32  
 25  EBITDA                     16 non-null     float64
 26  Cash Burn                  56 non-null     object 
 27  SKUs                       27 non-null     float64
 28  Has Patents                39 non-null     object 
 29  Bootstrapped               30 non-null     object 
 30  Original Ask Amount        441 non-null    float64
 31  Original Offered Equity    441 non-null    float64
 32  Valuation Requested        441 non-null    float64
 33  Received Offer             441 non-null    Int32  
 34  Accepted Offer             301 non-null    Int32  
 35  Total Deal Amount          248 non-null    float64
 36  Total Deal Equity          248 non-null    float64
 37  Total Deal Debt            53 non-null     float64
 38  Debt Interest              37 non-null     float64
 39  Deal Valuation             247 non-null    float64
 40  Number of Sharks in Deal   248 non-null    float64
 41  Deal Has Conditions        26 non-null     object 
 42  Royalty Deal               14 non-null     float64
 43  Advisory Shares Equity     3 non-null      float64
 44  Namita Investment Amount   81 non-null     float64
 45  Namita Investment Equity   81 non-null     float64
 46  Namita Debt Amount         14 non-null     float64
 47  Vineeta Investment Amount  63 non-null     float64
 48  Vineeta Investment Equity  63 non-null     float64
 49  Vineeta Debt Amount        11 non-null     float64
 50  Anupam Investment Amount   70 non-null     float64
 51  Anupam Investment Equity   70 non-null     float64
 52  Anupam Debt Amount         7 non-null      float64
 53  Aman Investment Amount     101 non-null    float64
 54  Aman Investment Equity     101 non-null    float64
 55  Aman Debt Amount           14 non-null     float64
 56  Peyush Investment Amount   87 non-null     float64
 57  Peyush Investment Equity   87 non-null     float64
 58  Peyush Debt Amount         11 non-null     float64
 59  Amit Investment Amount     33 non-null     float64
 60  Amit Investment Equity     33 non-null     float64
 61  Amit Debt Amount           6 non-null      float64
 62  Ashneer Investment Amount  21 non-null     float64
 63  Ashneer Investment Equity  21 non-null     float64
 64  Ashneer Debt Amount        2 non-null      float64
 65  Guest Investment Amount    37 non-null     float64
 66  Guest Investment Equity    37 non-null     float64
 67  Guest Debt Amount          6 non-null      float64
 68  Invested Guest Name        37 non-null     object 
 69  All Guest Names            116 non-null    object 
 70  Namita Present             361 non-null    float64
 71  Vineeta Present            287 non-null    float64
 72  Anupam Present             390 non-null    float64
 73  Aman Present               383 non-null    float64
 74  Peyush Present             295 non-null    float64
 75  Amit Present               128 non-null    float64
 76  Ashneer Present            99 non-null     float64
 77  Guest Present              116 non-null    float64
dtypes: Int32(14), float64(46), object(18)
memory usage: 250.8+ KB


shark_tank.describe().T.round(2).style.background_gradient(cmap = 'Oranges')


# Unique values in each column
for col in shark_tank.columns:
    print("Number of unique values in", col, "-", shark_tank[col].nunique())

Number of unique values in Season Number - 3
Number of unique values in Startup Name - 441
Number of unique values in Episode Number - 52
Number of unique values in Pitch Number - 441
Number of unique values in Season Start - 3
Number of unique values in Season End - 2
Number of unique values in Original Air Date - 125
Number of unique values in Episode Title - 126
Number of unique values in Anchor - 2
Number of unique values in Industry - 16
Number of unique values in Business Description - 438
Number of unique values in Company Website - 430
Number of unique values in Started in - 16
Number of unique values in Number of Presenters - 6
Number of unique values in Male Presenters - 6
Number of unique values in Female Presenters - 3
Number of unique values in Transgender Presenters - 1
Number of unique values in Couple Presenters - 2
Number of unique values in Pitchers Average Age - 3
Number of unique values in Pitchers City - 104
Number of unique values in Pitchers State - 42
Number of unique values in Yearly Revenue - 119
Number of unique values in Monthly Sales - 99
Number of unique values in Gross Margin - 42
Number of unique values in Net Margin - 30
Number of unique values in EBITDA - 14
Number of unique values in Cash Burn - 1
Number of unique values in SKUs - 23
Number of unique values in Has Patents - 2
Number of unique values in Bootstrapped - 2
Number of unique values in Original Ask Amount - 48
Number of unique values in Original Offered Equity - 31
Number of unique values in Valuation Requested - 97
Number of unique values in Received Offer - 2
Number of unique values in Accepted Offer - 2
Number of unique values in Total Deal Amount - 39
Number of unique values in Total Deal Equity - 49
Number of unique values in Total Deal Debt - 18
Number of unique values in Debt Interest - 8
Number of unique values in Deal Valuation - 93
Number of unique values in Number of Sharks in Deal - 5
Number of unique values in Deal Has Conditions - 1
Number of unique values in Royalty Deal - 1
Number of unique values in Advisory Shares Equity - 3
Number of unique values in Namita Investment Amount - 30
Number of unique values in Namita Investment Equity - 33
Number of unique values in Namita Debt Amount - 9
Number of unique values in Vineeta Investment Amount - 24
Number of unique values in Vineeta Investment Equity - 31
Number of unique values in Vineeta Debt Amount - 7
Number of unique values in Anupam Investment Amount - 29
Number of unique values in Anupam Investment Equity - 38
Number of unique values in Anupam Debt Amount - 6
Number of unique values in Aman Investment Amount - 35
Number of unique values in Aman Investment Equity - 40
Number of unique values in Aman Debt Amount - 11
Number of unique values in Peyush Investment Amount - 29
Number of unique values in Peyush Investment Equity - 35
Number of unique values in Peyush Debt Amount - 7
Number of unique values in Amit Investment Amount - 19
Number of unique values in Amit Investment Equity - 17
Number of unique values in Amit Debt Amount - 5
Number of unique values in Ashneer Investment Amount - 9
Number of unique values in Ashneer Investment Equity - 14
Number of unique values in Ashneer Debt Amount - 2
Number of unique values in Guest Investment Amount - 22
Number of unique values in Guest Investment Equity - 23
Number of unique values in Guest Debt Amount - 5
Number of unique values in Invested Guest Name - 9
Number of unique values in All Guest Names - 8
Number of unique values in Namita Present - 1
Number of unique values in Vineeta Present - 1
Number of unique values in Anupam Present - 1
Number of unique values in Aman Present - 1
Number of unique values in Peyush Present - 1
Number of unique values in Amit Present - 1
Number of unique values in Ashneer Present - 1
Number of unique values in Guest Present - 2


shark_tank_season1 = shark_tank.loc[shark_tank['Season Number']==1]
shark_tank_season1_without_unseen = shark_tank.loc[(shark_tank['Season Number']==1) & (shark_tank['Episode Number']!=0)]
shark_tank_season2 = shark_tank.loc[shark_tank['Season Number']==2]
shark_tank_season3 = shark_tank.loc[(shark_tank['Season Number']==3) | (shark_tank['Season Number'].isnull())]


# Data set information
print(shark_tank['Season Number'].max(), "total seasons in Indian SharkTank \n")
print(shark_tank['Pitch Number'].max(), "#startups came for pitching \n")
print("In Season 1, in", shark_tank_season1['Episode Number'].max(), "episodes, there were", shark_tank_season1.loc[shark_tank_season1['Episode Number']!=0]['Startup Name'].count(), "(real) pitches and", shark_tank_season1.loc[shark_tank_season1['Episode Number']==0]['Startup Name'].count(),"unseen pitches\n")
print("In Season 2, in", shark_tank_season2['Episode Number'].max(), "episodes, there were", shark_tank_season2.loc[shark_tank_season2['Episode Number']!=0]['Startup Name'].count(), "(real) pitches and", shark_tank_season2.loc[shark_tank_season2['Episode Number']==0]['Startup Name'].count(),"unseen pitch\n")
print("In Season 3, in", shark_tank_season3['Episode Number'].max(), "episodes, there were", shark_tank_season3.loc[shark_tank_season3['Episode Number']!=0]['Startup Name'].count(), "(real) pitches\n")

3 total seasons in Indian SharkTank 

441 #startups came for pitching 

In Season 1, in 36 episodes, there were 122 (real) pitches and 30 unseen pitches

In Season 2, in 51 episodes, there were 168 (real) pitches and 1 unseen pitch

In Season 3, in 40 episodes, there were 120 (real) pitches


# Season-wise number of episodes
pd.pivot_table(shark_tank, values='Episode Number', columns='Season Number', aggfunc='max')


# There were 2 to 4 pitches, in each episode
print(shark_tank.loc[shark_tank['Episode Number']!=0][['Season Number','Episode Number']].value_counts().sort_values(ascending=True).unique())

[2 3 4]


import pandas as pd
import matplotlib.pyplot as plt

# Load the data
data = pd.read_csv("shark Tank India.csv")

# Group by industry and count occurrences
industry_counts = data['Industry'].value_counts()

# Plot the graph
plt.figure(figsize=(10, 6))
industry_counts.plot(kind='bar', color='skyblue')
plt.title('Types of Industries Came for Investment in Shark Tank India')
plt.xlabel('Industry')
plt.ylabel('Number of Appearances')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()


import pandas as pd
import matplotlib.pyplot as plt

# Load the CSV data into a DataFrame
df = pd.read_csv('shark Tank India.csv')

# Assuming the CSV file has a column named 'Industry' which contains types of industries
# You can adjust the column name according to your CSV structure

# Count the occurrences of each industry
industry_counts = df['Industry'].value_counts()

# Plotting
plt.figure(figsize=(10, 6))
industry_counts.plot(kind='bar', color='skyblue')
plt.title('Types of Industries in Shark Tank India (Latest Season)')
plt.xlabel('Industry')
plt.ylabel('Number of Investments')
plt.xticks(rotation=45, ha='right')  # Rotate x-axis labels for better visibility
plt.tight_layout()  # Adjust layout to prevent clipping of labels
plt.show()


# Gender wise
print("Total pitchers -", int(shark_tank['Number of Presenters'].sum()), "\n")
print("")
print("Total male pitchers -", int(shark_tank['Male Presenters'].sum()), "\n")
print("Total female pitchers -", int(shark_tank['Female Presenters'].sum()), "\n")
print("Total transgender pitchers -", int(shark_tank['Transgender Presenters'].sum()), "\n")
print("")
print("COVID entrepreneurs/startups - ", shark_tank.loc[shark_tank['Started in']==2020]['Startup Name'].count(), sep='')

Total pitchers - 665 


Total male pitchers - 484 

Total female pitchers - 178 

Total transgender pitchers - 3 


COVID entrepreneurs/startups - 31


print("Male entrepreneurs percentage - ", round(shark_tank['Male Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100, 2), "%\n", sep='')
print("Female entrepreneurs percentage - ", round(shark_tank['Female Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100, 2), "%\n", sep='')
print("Transgender entrepreneurs percentage - ", round(shark_tank['Transgender Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100, 2), "%\n", sep='')
print("Couple entrepreneurs percentage - ", round(shark_tank.loc[shark_tank['Couple Presenters']==1]['Couple Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100, 0), "%\n", sep='')
print("")

fig = plt.figure(figsize =(10, 7))
plt.title("Pitchers Gender wise percentage")
plt.pie([round(shark_tank['Male Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100, 2), round(shark_tank['Female Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100, 2), round(shark_tank['Transgender Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100, 2)], labels = ["Male","Female","Transgender"], autopct='%.1f%%', colors=["lightblue", "pink", "gray"], textprops={'fontsize': 14})
plt.show()

Male entrepreneurs percentage - 72.78%

Female entrepreneurs percentage - 26.77%

Transgender entrepreneurs percentage - 0.45%

Couple entrepreneurs percentage - 9.0%


# Age wise
print(shark_tank['Pitchers Average Age'].value_counts(),"\n")

# In percentage
print(round(shark_tank['Pitchers Average Age'].value_counts(normalize=True)*100).astype(str).str.replace('.0', '%', regex=False),"\n")

plt.title("Pitchers Age wise percentage")
shark_tank["Pitchers Average Age"].value_counts().plot(kind='pie', autopct='%.0f%%', cmap='tab20c', fontsize=14)
plt.ylabel('')

Pitchers Average Age
Middle    231
Young      88
Old         1
Name: count, dtype: int64 

Pitchers Average Age
Middle    72%
Young     28%
Old        0%
Name: proportion, dtype: object

Text(0, 0.5, '')


# Offers received
print(shark_tank['Received Offer'].value_counts(), "\n")
print(round(shark_tank['Received Offer'].value_counts(normalize=True)*100).astype(str).str.replace('.0', '%', regex=False))

plt.figure(figsize = (15,9))
ax1 = plt.subplot(221)
shark_tank["Received Offer"].value_counts().plot(kind='bar', color=["limegreen","crimson"], ec="k")
plt.xlabel("Number of Offers Received / Not Received")
plt.yticks([])
plt.xticks(rotation=0)
for x,y in enumerate(shark_tank["Received Offer"].value_counts()):
    plt.annotate(y, (x,y), fontsize=13, color="blue")
    
ax2 = plt.subplot(222)
shark_tank["Received Offer"].value_counts().plot(kind='pie', autopct='%.0f%%', explode = (0,0.05), colors=["limegreen","crimson"], shadow=True, fontsize=13)
plt.ylabel('')

# 272 companies received offers & 124 startups could not convince #Sharks to invest.

Received Offer
1    216
0    104
Name: count, dtype: int64 

Received Offer
1    68%
0    32%
Name: proportion, dtype: object

Text(0, 0.5, '')


# Offers accepted
print(shark_tank['Accepted Offer'].value_counts(), "\n")
print(round(shark_tank['Accepted Offer'].value_counts(normalize=True)*100).astype(str).str.replace('.0', '%', regex=False))

plt.figure(figsize = (15, 9))
ax1 = plt.subplot(221)
shark_tank["Accepted Offer"].value_counts().plot(kind='bar', color=["limegreen","crimson"], ec="k")
plt.xlabel("Number of Offers Accepted / Rejected")
plt.yticks([])
plt.xticks(rotation = 0)
for x,y in enumerate(shark_tank["Accepted Offer"].value_counts()):
    plt.annotate(y, (x,y), fontsize=13, color="blue")
    
ax2 = plt.subplot(222)
shark_tank["Accepted Offer"].value_counts().plot(kind='pie', autopct='%.0f', explode = (0,0.05), colors=["limegreen","crimson"], shadow=True, fontsize=13)
plt.ylabel('')

# 220 companies accepted investments they got & 52 #Startups did not accept Shark's offer.

Accepted Offer
1.0    176
0.0     40
Name: count, dtype: int64 

Accepted Offer
1.0    81%
0.0    19%
Name: proportion, dtype: object

Text(0, 0.5, '')


# Offers rejected by pitchers/startup companies
print(shark_tank[shark_tank['Accepted Offer']==0]["Startup Name"].count())
shark_tank.loc[shark_tank['Accepted Offer']==0, ["Season Number","Startup Name","Industry","Original Ask Amount","Original Offered Equity"]]

40


# Maximum amount requested
print("Maximum amount requested, by a pitcher - Rs.", round(shark_tank["Original Ask Amount"].max()/100), "crores")

Maximum amount requested, by a pitcher - Rs. 300 crores


# Least amount requested
print("Least amount requested, by a pitcher - Rs.", round(shark_tank["Original Ask Amount"].min()*100000))

Least amount requested, by a pitcher - Rs. 0


# Sum of investment amount asked, in Shark Tank, in India
print("Sum of investment amount asked, by all startup companies, in Indian Shark Tank -", format_currency(shark_tank['Original Ask Amount'].sum()/100, 'INR', locale='en_IN').replace(".00", ""), "crores")

Sum of investment amount asked, by all startup companies, in Indian Shark Tank - ₹540.41 crores


# Amount invested by all sharks, in India SharkTank
print("Amount invested by all sharks, in Shark Tank India -", format_currency(shark_tank['Total Deal Amount'].sum()/100, 'INR', locale='en_IN').replace(".00", ""), "crores")

Amount invested by all sharks, in Shark Tank India - ₹110.06 crores


# Sum of loan/debt amount, in India Shark Tank
print("Sum of loan/debt amount, given by all sharks, in India SharkTank -", format_currency(shark_tank['Total Deal Debt'].sum()/100, 'INR', locale='en_IN').replace(".00", ""), "crores")

Sum of loan/debt amount, given by all sharks, in India SharkTank - ₹18.11 crores


# Top 20 investments (more than 1Cr), as per total investment/deal amount (in lakhs)
print(shark_tank.groupby('Startup Name')['Total Deal Amount'].max().nlargest(20))

tmpdf = shark_tank.sort_values('Total Deal Amount', ascending=False)[0:20]
fig = px.bar(tmpdf, x="Startup Name", y='Total Deal Amount', color="Startup Name", title="Highest Investment as per deal amount (in lakhs)", text=tmpdf['Total Deal Amount'])
fig.show()

Startup Name
MeduLance            200.0
Pharmallama          200.0
UnStop               200.0
AasVidyalaya         150.0
Portl                150.0
Snitch               150.0
Stage                150.0
Trunome              150.0
MindPeers            106.0
Annie                105.0
Broomees             100.0
GearHeadMotors       100.0
Geeani               100.0
Get-A-Whey           100.0
HammerLifestyle      100.0
Haqdarshak           100.0
Hoovu                100.0
HumpyA2              100.0
INACAN               100.0
InsuranceSamadhan    100.0
Name: Total Deal Amount, dtype: float64


# Top 20 investments, as per total equity/shares percentage diluted
print(shark_tank.groupby('Startup Name')['Total Deal Equity'].max().nlargest(20))

tmpdf = shark_tank.sort_values('Total Deal Equity', ascending=False)[0:20]
fig = px.bar(tmpdf, x="Startup Name", y='Total Deal Equity', color="Startup Name", title="Highest Investment as per Equity percentage", text=tmpdf['Total Deal Equity'].map(int).map(str) + "%")
fig.show()

Startup Name
Sid07Designs          75.00
BoozScooters          50.00
IsakFragrances        50.00
HammerLifestyle       40.00
KGAgrotech            40.00
TheSassBar            35.00
VivalyfInnovations    33.33
GoldSafeSolutions     30.00
HeartUpMySleeves      30.00
JainShikanji          30.00
ColourMeMad-CMM       25.00
CosIQ                 25.00
FindYourKicksIndia    25.00
HoloKitab             25.00
PNT                   25.00
Raasa                 25.00
LOKA                  24.00
TheQuirkyNaari        24.00
WakaoFoods            21.00
Angrakhaa             20.00
Name: Total Deal Equity, dtype: float64


# Startups who sold more than 1/3rd of their company (equity) to Sharks
print(shark_tank.loc[shark_tank['Total Deal Equity'] > 32 ][["Startup Name"]].count())
print(shark_tank.loc[shark_tank['Total Deal Equity'] > 32 ][["Season Number","Startup Name","Total Deal Amount", "Total Deal Equity"]])

tmpdf = shark_tank.loc[shark_tank['Total Deal Equity'] > 32 ].sort_values('Total Deal Equity', ascending=False)
fig = px.bar(tmpdf, x="Startup Name", y='Total Deal Equity', color="Startup Name", title="Startups who sold more than 1/3rd of their company", text=tmpdf['Total Deal Equity'].map(int).map(str) + "%")
fig.show()

Startup Name    7
dtype: int64
    Season Number        Startup Name  Total Deal Amount  Total Deal Equity
1               1        BoozScooters               40.0              50.00
23              1  VivalyfInnovations               56.0              33.33
43              1     HammerLifestyle              100.0              40.00
66              1        Sid07Designs               25.0              75.00
76              1          TheSassBar               50.0              35.00
77              1          KGAgrotech               10.0              40.00
82              1      IsakFragrances               50.0              50.00


# Top 20 investments, as per total debt/loan amount
print(shark_tank.groupby('Startup Name')['Total Deal Debt'].max().nlargest(20))

tmpdf = shark_tank.sort_values('Total Deal Debt', ascending=False)[0:20]
fig = px.bar(tmpdf, x="Startup Name", y='Total Deal Debt', color="Startup Name", title="Highest Investment as per Debt amount (in lakhs)", text=tmpdf['Total Deal Debt'])
fig.show()

Startup Name
Stage                150.0
WatchoutWearables    100.0
uBreathe             100.0
Otua                  99.0
Wol3D                 70.0
TAC                   69.0
maisha                65.0
Hood                  60.0
iMumz                 60.0
AyuSynk               50.0
DailyDump             50.0
Freebowler            50.0
GROWiT                50.0
LilGoodness           50.0
NamhyaFoods           50.0
NutriCook             50.0
Rubans                50.0
StoreMyGoods          50.0
Aadvik                45.0
VSMani                41.0
Name: Total Deal Debt, dtype: float64


# Startups who got Debt/loan amount
print("Number of startups who got debt/loan amount", shark_tank['Total Deal Debt'].count(),"\n")
shark_tank.loc[shark_tank['Total Deal Debt'] > 0][["Season Number","Startup Name","Total Deal Amount","Total Deal Equity","Total Deal Debt"]]

Number of startups who got debt/loan amount 39


# Startups who gave Royalty
print("Number of startups who gave Royalty", shark_tank['Royalty Deal'].count(),"\n")

shark_tank.loc[shark_tank['Royalty Deal'] == 1][["Season Number","Startup Name","Total Deal Amount","Total Deal Equity"]]

Number of startups who gave Royalty 14


# Startups who gave Advisory shares
print("Number of startups who gave Advisory shares/equity", shark_tank['Advisory Shares Equity'].count(),"\n")

shark_tank.loc[shark_tank['Advisory Shares Equity'] > 0][["Season Number","Startup Name","Total Deal Amount","Total Deal Equity", "Advisory Shares Equity"]]

# DATA INCOMPLETE

Number of startups who gave Advisory shares/equity 3


# Deals with conditions
print("Number of startups who accepted for conditional deals", shark_tank['Deal Has Conditions'].count(),"\n")

shark_tank.loc[shark_tank['Deal Has Conditions'] == 'yes'][["Season Number","Startup Name","Total Deal Amount","Total Deal Equity", "Advisory Shares Equity"]]

Number of startups who accepted for conditional deals 26


# Amount Invested by sharks, in all seasons
Amount = [shark_tank['Ashneer Investment Amount'].sum(), shark_tank['Namita Investment Amount'].sum(), shark_tank['Anupam Investment Amount'].sum(), shark_tank['Vineeta Investment Amount'].sum(),
    shark_tank['Aman Investment Amount'].sum(), shark_tank['Peyush Investment Amount'].sum(), shark_tank['Guest Investment Amount'].sum()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush', 'Guests']
df = {'Name':name, 'Total Amount':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Amount'])
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
    plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Investment Amount (in lakhs) by Sharks, in all seasons", fontsize=14)
plt.show()

# Aman invested maximum amount, in all seasons - 35 crores
# Ashneer invested minimum amount, in all seasons - 5 crores


# Amount Invested by sharks
# Season 1
Amount = [shark_tank_season1['Ashneer Investment Amount'].sum(), shark_tank_season1['Namita Investment Amount'].sum(), shark_tank_season1['Anupam Investment Amount'].sum(), shark_tank_season1['Vineeta Investment Amount'].sum(),
    shark_tank_season1['Aman Investment Amount'].sum(), shark_tank_season1['Peyush Investment Amount'].sum(), shark_tank_season1['Guest Investment Amount'].sum()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush','Guests']
df = {'Name':name, 'Total Amount':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Amount'])
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
    plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Investment Amount (in lakhs) by Sharks, in Season 1", fontsize=14)
plt.show()

# Season 2
Amount = [shark_tank_season2['Namita Investment Amount'].sum(), shark_tank_season2['Anupam Investment Amount'].sum(), shark_tank_season2['Vineeta Investment Amount'].sum(),
    shark_tank_season2['Aman Investment Amount'].sum(), shark_tank_season2['Peyush Investment Amount'].sum(), shark_tank_season2['Amit Investment Amount'].sum(), shark_tank_season2['Guest Investment Amount'].sum()]
name=['Namita','Anupam','Vineeta','Aman','Peyush','Amit','Guests']
df = {'Name':name, 'Total Amount':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Amount'])
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
    plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Investment Amount (in lakhs) by Sharks, in Season 2", fontsize=14)
plt.show()

# Season 3
Amount = [shark_tank_season3['Namita Investment Amount'].sum(), shark_tank_season3['Anupam Investment Amount'].sum(), shark_tank_season3['Vineeta Investment Amount'].sum(),
    shark_tank_season3['Aman Investment Amount'].sum(), shark_tank_season3['Peyush Investment Amount'].sum(), shark_tank_season3['Amit Investment Amount'].sum(), shark_tank_season3['Guest Investment Amount'].sum()]
name=['Namita','Anupam','Vineeta','Aman','Peyush','Amit','Guests']
df = {'Name':name, 'Total Amount':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Amount'])
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
    plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Investment Amount (in lakhs) by Sharks, in Season 3", fontsize=14)
plt.show()


# Equity received by sharks, in all seasons
Amount = [shark_tank['Ashneer Investment Equity'].sum(), shark_tank['Namita Investment Equity'].sum(), shark_tank['Anupam Investment Equity'].sum(), shark_tank['Vineeta Investment Equity'].sum(),
    shark_tank['Aman Investment Equity'].sum(), shark_tank['Peyush Investment Equity'].sum(), shark_tank['Guest Investment Equity'].sum()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush', 'Guests']
df = {'Name':name, 'Total Equity':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Equity'], color='g')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
    plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total equity received (in %), by Sharks, in all companies, in all seasons", fontsize=15)
plt.show()

# Peyush got maximum equity of - 494% in different companies, in all seasons
# Ashneer got minimum equity of - 93% in different companies, in all seasons


# Equity received by sharks
# Season 1
Equity = [shark_tank_season1['Ashneer Investment Equity'].sum(), shark_tank_season1['Namita Investment Equity'].sum(), shark_tank_season1['Anupam Investment Equity'].sum(), shark_tank_season1['Vineeta Investment Equity'].sum(),
    shark_tank_season1['Aman Investment Equity'].sum(), shark_tank_season1['Peyush Investment Equity'].sum(), shark_tank_season1['Guest Investment Equity'].sum()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush','Guests']
df = {'Name':name, 'Total Equity':Equity}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Equity'], color='g')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Equity):
    plt.text(x=index, y =d+1, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Equity received (in %) by Sharks, in all companies, in Season 1", fontsize=14)
plt.show()

# Season 2
Equity = [shark_tank_season2['Namita Investment Equity'].sum(), shark_tank_season2['Anupam Investment Equity'].sum(), shark_tank_season2['Vineeta Investment Equity'].sum(),
    shark_tank_season2['Aman Investment Equity'].sum(), shark_tank_season2['Peyush Investment Equity'].sum(), shark_tank_season2['Amit Investment Equity'].sum(), shark_tank_season2['Guest Investment Equity'].sum()]
name=['Namita','Anupam','Vineeta','Aman','Peyush','Amit', 'Guests']
df = {'Name':name, 'Total Equity':Equity}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Equity'], color='g')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Equity):
    plt.text(x=index, y =d+1, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Equity received (in %) by Sharks, in all companies, in Season 2", fontsize=14)
plt.show()

# Season 3
Equity = [shark_tank_season3['Namita Investment Equity'].sum(), shark_tank_season3['Anupam Investment Equity'].sum(), shark_tank_season3['Vineeta Investment Equity'].sum(),
    shark_tank_season3['Aman Investment Equity'].sum(), shark_tank_season3['Peyush Investment Equity'].sum(), shark_tank_season3['Amit Investment Equity'].sum(), shark_tank_season3['Guest Investment Equity'].sum()]
name=['Namita','Anupam','Vineeta','Aman','Peyush','Amit', 'Guests']
df = {'Name':name, 'Total Equity':Equity}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Equity'], color='g')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Equity):
    plt.text(x=index, y =d+1, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Equity received (in %) by Sharks, in all companies, in Season 3", fontsize=14)
plt.show()


# Investment based on the  Debt/loaned Amount, in all seasons
Amount = [shark_tank['Ashneer Debt Amount'].sum(), shark_tank['Namita Debt Amount'].sum(), shark_tank['Anupam Debt Amount'].sum(), shark_tank['Vineeta Debt Amount'].sum(),
    shark_tank['Aman Debt Amount'].sum(), shark_tank['Peyush Debt Amount'].sum(), shark_tank['Guest Debt Amount'].sum()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush', 'Guests']
df = {'Name':name, 'Total Equity':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Equity'], color='c')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
    plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Debt amount (in lakhs) given by Sharks, in all seasons", fontsize=15)
plt.show()

# Namita gave maximum debt amount, in all seasons - 5 crores
# All guests gave minimum debt amount, in all seasons - 0.87 crores


# Investment based on the  Debt/loaned Amount
# Season 1
debt = [shark_tank_season1['Ashneer Debt Amount'].sum(), shark_tank_season1['Namita Debt Amount'].sum(), shark_tank_season1['Anupam Debt Amount'].sum(), shark_tank_season1['Vineeta Debt Amount'].sum(),
    shark_tank_season1['Aman Debt Amount'].sum(), shark_tank_season1['Peyush Debt Amount'].sum(), shark_tank_season1['Guest Debt Amount'].sum()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush','Guests']
df = {'Name':name, 'Total debt':debt}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total debt'], color='c')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(debt):
    plt.text(x=index, y =d, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Debt amount (in lakhs) given by Sharks, in Season 1", fontsize=14)
plt.show()

# Season 2
debt = [shark_tank_season2['Namita Debt Amount'].sum(), shark_tank_season2['Anupam Debt Amount'].sum(), shark_tank_season2['Vineeta Debt Amount'].sum(),
    shark_tank_season2['Aman Debt Amount'].sum(), shark_tank_season2['Peyush Debt Amount'].sum(), shark_tank_season2['Amit Debt Amount'].sum(), shark_tank_season2['Guest Debt Amount'].sum()]
name=['Namita','Anupam','Vineeta','Aman','Peyush','Amit', 'Guests']
df = {'Name':name, 'Total debt':debt}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total debt'], color='c')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(debt):
    plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Debt amount (in lakhs) given by Sharks, in Season 2", fontsize=14)
plt.show()

# Season 3
debt = [shark_tank_season3['Namita Debt Amount'].sum(), shark_tank_season3['Anupam Debt Amount'].sum(), shark_tank_season3['Vineeta Debt Amount'].sum(),
    shark_tank_season3['Aman Debt Amount'].sum(), shark_tank_season3['Peyush Debt Amount'].sum(), shark_tank_season3['Amit Debt Amount'].sum(), shark_tank_season3['Guest Debt Amount'].sum()]
name=['Namita','Anupam','Vineeta','Aman','Peyush','Amit', 'Guests']
df = {'Name':name, 'Total debt':debt}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total debt'], color='c')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(debt):
    plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Debt amount (in lakhs) given by Sharks, in Season 3", fontsize=14)
plt.show()

print("Namita gave 20% higher loan in season 2, compared to season 1\nAman started giving more debt/loan in season 3")

Namita gave 20% higher loan in season 2, compared to season 1
Aman started giving more debt/loan in season 3


# Number of companies invested, in all seasons
Amount = [shark_tank['Ashneer Investment Amount'].count(), shark_tank['Namita Investment Amount'].count(), shark_tank['Anupam Investment Amount'].count(), shark_tank['Vineeta Investment Amount'].count(),
    shark_tank['Aman Investment Amount'].count(), shark_tank['Peyush Investment Amount'].count(), shark_tank['Guest Investment Amount'].count()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush','Guests']
df = {'Name':name, 'Total Amount':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Amount'], color='pink')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
    plt.text(x=index, y =d, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Number of companies invested, in all seasons", fontsize=14)
plt.show()


# Number of companies invested
# Season 1
Amount = [shark_tank_season1['Ashneer Investment Amount'].count(), shark_tank_season1['Namita Investment Amount'].count(), shark_tank_season1['Anupam Investment Amount'].count(), shark_tank_season1['Vineeta Investment Amount'].count(),
    shark_tank_season1['Aman Investment Amount'].count(), shark_tank_season1['Peyush Investment Amount'].count(), shark_tank_season1['Guest Investment Amount'].count()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush','Guests']
df = {'Name':name, 'Total Amount':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Amount'], color='pink')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
    plt.text(x=index, y =d, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Number of companies invested, in Season 1", fontsize=14)
plt.show()

# Season 2
Amount = [shark_tank_season2['Namita Investment Amount'].count(), shark_tank_season2['Anupam Investment Amount'].count(), shark_tank_season2['Vineeta Investment Amount'].count(),
    shark_tank_season2['Aman Investment Amount'].count(), shark_tank_season2['Peyush Investment Amount'].count(), shark_tank_season2['Amit Investment Amount'].count(), shark_tank_season2['Guest Investment Amount'].count()]
name=['Namita','Anupam','Vineeta','Aman','Peyush','Amit','Guests']
df = {'Name':name, 'Total Amount':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Amount'], color='pink')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
    plt.text(x=index, y =d, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Number of companies invested, in Season 2", fontsize=14)
plt.show()

# Season 3
Amount = [shark_tank_season3['Namita Investment Amount'].count(), shark_tank_season3['Anupam Investment Amount'].count(), shark_tank_season3['Vineeta Investment Amount'].count(),
    shark_tank_season3['Aman Investment Amount'].count(), shark_tank_season3['Peyush Investment Amount'].count(), shark_tank_season3['Amit Investment Amount'].count(), shark_tank_season3['Guest Investment Amount'].count()]
name=['Namita','Anupam','Vineeta','Aman','Peyush','Amit','Guests']
df = {'Name':name, 'Total Amount':Amount}
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Amount'], color='pink')
plt.xticks(fontsize=14)
plt.yticks([])
for index,d in enumerate(Amount):
    plt.text(x=index, y =d, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Number of companies invested, in Season 3", fontsize=14)
plt.show()


# Word cloud based on Startup Names, in all seasons
text = " Shark Tank India ".join(cat for cat in shark_tank['Startup Name'])
stop_words = list(STOPWORDS)
wordcloud = WordCloud(width=2000, height=1500, stopwords=stop_words, background_color='black', colormap='Set2', collocations=False, random_state=2024).generate(text)
plt.figure(figsize=(25,20))
plt.imshow(wordcloud)
plt.axis("off")
plt.show()


# Word cloud based on Startup Names, in current/latest season (3rd season)
text = " Shark Tank India ".join(cat for cat in shark_tank_season3['Startup Name'])
stop_words = list(STOPWORDS)
wordcloud = WordCloud(width=1800, height=1300, stopwords=stop_words, background_color='black', colormap='Set3', collocations=False, random_state=2024).generate(text)
plt.figure(figsize=(14,14))
plt.imshow(wordcloud)
plt.axis("off")
plt.show()


print("Total investments by Ashneer", shark_tank[shark_tank['Ashneer Investment Amount']>=0][['Ashneer Investment Amount']].count().to_string()[-2:])
print("Investment amount by Ashneer", round(shark_tank['Ashneer Investment Amount'].sum()/100, 2), "crores")
print("Equity received by Ashneer", round(shark_tank['Ashneer Investment Equity'].sum(), 2), "% in different companies")
print("Debt/loan amount by Ashneer", round(shark_tank['Ashneer Debt Amount'].sum()/100, 2), "crores\n")

print("Company details:")
print('-'*75)
print(shark_tank.loc[shark_tank['Ashneer Investment Amount']>=0][["Startup Name","Industry","Ashneer Investment Amount"]].to_string(index=False))
print('-'*75)

print("\nAshneer industry wise investments\n")
print(shark_tank[shark_tank['Ashneer Investment Amount']>=0]['Industry'].value_counts().sort_values(ascending=False))
plt.figure(figsize = (10,6))
shark_tank[shark_tank['Ashneer Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.ylabel('')
plt.show()

tmpdf = shark_tank.loc[shark_tank['Ashneer Investment Amount']>=0] [["Startup Name","Ashneer Investment Amount","Ashneer Investment Equity"]].sort_values(by="Ashneer Investment Equity")
fig = px.treemap(tmpdf, path=['Startup Name'], values=tmpdf['Ashneer Investment Amount'], width=800, height=800)
fig.update_layout(margin = dict(t=5, l=5, r=5, b=5))
fig.update_traces(textposition='middle center')
fig.show()

Total investments by Ashneer 21
Investment amount by Ashneer 5.39 crores
Equity received by Ashneer 93.24 % in different companies
Debt/loan amount by Ashneer 1.14 crores

Company details:
---------------------------------------------------------------------------
      Startup Name                     Industry  Ashneer Investment Amount
     BluePineFoods                         Food                      25.00
      BoozScooters Vehicles/Electrical Vehicles                      20.00
         TagzFoods                         Food                      70.00
     SkippiIcePops                         Food                      20.00
 RaisingSuperstars                    Education                      50.00
       BeyondSnack                         Food                      25.00
      MotionBreeze Vehicles/Electrical Vehicles                      30.00
         EventBeep                    Education                      10.00
     TheYarnBazaar                Manufacturing                      25.00
       BambooIndia                Manufacturing                      25.00
FindYourKicksIndia               Beauty/Fashion                      10.00
      AasVidyalaya                    Education                      50.00
              Otua Vehicles/Electrical Vehicles                       1.00
           WeSTOCK                  Animal/Pets                      15.00
            INACAN                         Food                      20.00
        Get-A-Whey                         Food                      33.33
     HairOriginals               Beauty/Fashion                      20.00
         TweekLabs                       Sports                      20.00
            Proxgy          Technology/Software                      50.00
  NomadFoodProject                         Food                      10.00
      JainShikanji                         Food                      10.00
---------------------------------------------------------------------------

Ashneer industry wise investments

Industry
Food                            8
Vehicles/Electrical Vehicles    3
Education                       3
Manufacturing                   2
Beauty/Fashion                  2
Animal/Pets                     1
Sports                          1
Technology/Software             1
Name: count, dtype: int64


print("Total investments by Namita", shark_tank[shark_tank['Namita Investment Amount']>0][['Namita Investment Amount']].count().to_string()[-2:])
print("Investment amount by Namita", round(shark_tank['Namita Investment Amount'].sum()/100, 2), "crores")
print("Equity received by Namita", round(shark_tank['Namita Investment Equity'].sum(), 2), "% in different companies")
print("Debt/loan amount by Namita", round(shark_tank['Namita Debt Amount'].sum()/100, 2), "crores\n")

print("Company details:")
print('-'*75)
print(shark_tank.loc[shark_tank['Namita Investment Amount']>0][["Startup Name","Industry","Namita Investment Amount"]].to_string(index=False))
print('-'*75)

print("\nNamita industry wise investments\n")
print(shark_tank[shark_tank['Namita Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False))
plt.figure(figsize = (10,6))
shark_tank[shark_tank['Namita Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.ylabel('')
plt.show()

tmpdf = shark_tank.loc[shark_tank['Namita Investment Amount']>0] [["Startup Name","Namita Investment Amount","Namita Investment Equity"]].sort_values(by="Namita Investment Equity")
fig = px.treemap(tmpdf, path=['Startup Name'], values=tmpdf['Namita Investment Amount'], width=800, height=800)
fig.update_layout(margin = dict(t=5, l=5, r=5, b=5))
fig.update_traces(textposition='middle center')
fig.show()

Total investments by Namita 66
Investment amount by Namita 20.98 crores
Equity received by Namita 301.59 % in different companies
Debt/loan amount by Namita 5.25 crores

Company details:
---------------------------------------------------------------------------
      Startup Name             Industry  Namita Investment Amount
       JhaJiAchaar                 Food                 28.300000
            Bummer       Beauty/Fashion                 37.500000
     SkippiIcePops                 Food                 20.000000
      Menstrupedia            Education                 50.000000
             Altor        Manufacturing                 25.000000
           Nuutjob       Beauty/Fashion                  8.330000
             Farda       Beauty/Fashion                 15.000000
              Auli       Beauty/Fashion                 75.000000
             Annie            Education                 35.000000
   TheRenalProject       Medical/Health                 50.000000
           Cocofit                 Food                  0.000016
       BeyondWater                 Food                 37.500000
FindYourKicksIndia       Beauty/Fashion                 10.000000
      AasVidyalaya            Education                 50.000000
           WeSTOCK          Animal/Pets                 15.000000
            INACAN                 Food                 20.000000
SunfoxTechnologies       Medical/Health                 20.000000
        RarePlanet        Manufacturing                 65.000000
 WattTechnovations       Medical/Health                  0.000253
        WakaoFoods                 Food                 25.000000
       KabaddiAdda               Sports                 40.000000
   ColourMeMad-CMM       Beauty/Fashion                 40.000000
  NomadFoodProject                 Food                 10.000000
          SneaKare       Beauty/Fashion                  7.000000
      StoreMyGoods             Services                 25.000000
    VeryMuchIndian       Beauty/Fashion                 25.000000
             Stage        Entertainment                 50.000000
            Girgit       Beauty/Fashion                 20.000000
       Brandsdaddy        Manufacturing                 35.000000
        Haqdarshak             Services                 33.330000
           AyuSynk       Medical/Health                 50.000000
 AtypicalAdvantage  Technology/Software                 15.000000
         Nestroots Furnishing/Household                 50.000000
        Freebowler               Sports                 25.000000
         DailyDump        Manufacturing                 30.000000
           Janitri       Medical/Health                100.000000
         InsideFPV        Manufacturing                 18.750000
             Pflow       Medical/Health                 30.000000
            VSMani                 Food                 19.000000
        SpiceStory                 Food                 70.000000
            Snitch       Beauty/Fashion                 30.000000
             Portl             Services                 50.000000
          Broomees             Services                 33.330000
           PadCare        Manufacturing                 25.000000
 SwadeshiBlessings Furnishing/Household                 12.500000
            UnStop  Technology/Software                 50.000000
         CloudWorx  Technology/Software                 20.000000
          Mahantam        Manufacturing                  6.000000
         MindPeers       Medical/Health                 17.660000
          DigiQure       Medical/Health                 40.000000
           Pabiben       Beauty/Fashion                 10.000000
          uBreathe        Manufacturing                 50.000000
           Perfora Furnishing/Household                 26.660000
         MeduLance       Medical/Health                 66.660000
         HoloKitab  Technology/Software                 45.000000
           GladFul                 Food                 16.660000
       Pharmallama       Medical/Health                 40.000000
            GROWiT          Agriculture                 25.000000
           funngro  Technology/Software                 25.000000
      LondonBubble                 Food                 75.000000
            Subhag       Medical/Health                 20.000000
  ThePlatedProject             Services                 25.000000
            SoulUp             Services                 50.000000
            Rubans       Beauty/Fashion                 33.330000
     ForeverModest       Beauty/Fashion                  5.000000
         Sahayatha       Medical/Health                 20.000000
---------------------------------------------------------------------------

Namita industry wise investments

Industry
Beauty/Fashion          13
Medical/Health          12
Food                    11
Manufacturing            8
Services                 6
Technology/Software      5
Education                3
Furnishing/Household     3
Sports                   2
Animal/Pets              1
Entertainment            1
Agriculture              1
Name: count, dtype: int64


print("Total investments by Anupam", shark_tank[shark_tank['Anupam Investment Amount']>=0][['Anupam Investment Amount']].count().to_string()[-2:])
print("Investment amount by Anupam", round(shark_tank['Anupam Investment Amount'].sum()/100, 2), "crores")
print("Equity received by Anupam", round(shark_tank['Anupam Investment Equity'].sum(), 2), "% in different companies")
print("Debt/loan amount by Anupam", round(shark_tank['Anupam Debt Amount'].sum()/100, 2), "crores\n")

print("Company details:")
print('-'*85)
print(shark_tank.loc[shark_tank['Anupam Investment Amount']>=0][["Startup Name","Industry","Anupam Investment Amount"]].to_string(index=False))
print('-'*85)

print("\nAnupam industry wise investments\n")
print(shark_tank[shark_tank['Anupam Investment Amount']>=0]['Industry'].value_counts().sort_values(ascending=False))
plt.figure(figsize = (10,6))
shark_tank[shark_tank['Anupam Investment Amount']>=0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.ylabel('')
plt.show()

tmpdf = shark_tank.loc[shark_tank['Anupam Investment Amount']>=0] [["Startup Name","Anupam Investment Amount","Anupam Investment Equity"]].sort_values(by="Anupam Investment Equity")
fig = px.treemap(tmpdf, path=['Startup Name'], values=tmpdf['Anupam Investment Amount'], width=800, height=800)
fig.update_layout(margin = dict(t=5, l=5, r=5, b=5))
fig.update_traces(textposition='middle center')
fig.show()

Total investments by Anupam 51
Investment amount by Anupam 14.51 crores
Equity received by Anupam 305.83 % in different companies
Debt/loan amount by Anupam 0.98 crores

Company details:
-------------------------------------------------------------------------------------
          Startup Name                     Industry  Anupam Investment Amount
      HeartUpMySleeves               Beauty/Fashion                 12.500000
                 CosIQ               Beauty/Fashion                 25.000000
            RevampMoto Vehicles/Electrical Vehicles                 50.000000
         SkippiIcePops                         Food                 20.000000
                Kavach                    Education                  2.500000
    VivalyfInnovations               Medical/Health                 28.000000
              Meatyour                         Food                 10.000000
ARRCOATSurfaceTextures                Manufacturing                 50.000000
                  LOKA          Technology/Software                 13.330000
                 Annie                    Education                 35.000000
            Carragreen                Manufacturing                 25.000000
         TheYarnBazaar                Manufacturing                 25.000000
               Cocofit                         Food                  0.000016
           BambooIndia                Manufacturing                 25.000000
              Let'sTry                         Food                 22.500000
    FindYourKicksIndia               Beauty/Fashion                 10.000000
                INACAN                         Food                 20.000000
        TheQuirkyNaari               Beauty/Fashion                 17.500000
         HairOriginals               Beauty/Fashion                 20.000000
            TheSassBar               Beauty/Fashion                 25.000000
             PawsIndia                  Animal/Pets                 50.000000
    SunfoxTechnologies               Medical/Health                 20.000000
     WattTechnovations               Medical/Health                  0.000253
             TweekLabs                       Sports                 20.000000
          JainShikanji                         Food                 10.000000
                 Dorji                         Food                 10.000000
     WatchoutWearables                  Electronics                 50.000000
             PatilKaki                         Food                 20.000000
               Winston               Beauty/Fashion                 50.000000
                TeaFit             Liquor/Beverages                 12.500000
           Zillionaire               Beauty/Fashion                100.000000
                 Kyari                Manufacturing                 25.500000
               Solinas                     Services                 45.000000
                 Raasa                         Food                 50.000000
                Snitch               Beauty/Fashion                 30.000000
                 Ravel               Beauty/Fashion                 75.000000
             HoneyVeda                         Food                 25.000000
               PadCare                Manufacturing                 25.000000
                Geeani Vehicles/Electrical Vehicles                 33.330000
                 Amore                         Food                 75.000000
        SharmaJiKiAata                         Food                 40.000000
                UnStop          Technology/Software                 50.000000
             CloudWorx          Technology/Software                 20.000000
              Mahantam                Manufacturing                  6.000000
           DhruvVidyut Vehicles/Electrical Vehicles                  0.000000
             Homestrap         Furnishing/Household                 50.000000
           Pharmallama               Medical/Health                 40.000000
               Trunome               Medical/Health                 37.500000
      What'sUpWellness                         Food                 20.000000
         ForeverModest               Beauty/Fashion                  5.000000
             Sahayatha               Medical/Health                 20.000000
-------------------------------------------------------------------------------------

Anupam industry wise investments

Industry
Food                            13
Beauty/Fashion                  11
Manufacturing                    7
Medical/Health                   6
Vehicles/Electrical Vehicles     3
Technology/Software              3
Education                        2
Animal/Pets                      1
Sports                           1
Electronics                      1
Liquor/Beverages                 1
Services                         1
Furnishing/Household             1
Name: count, dtype: int64


print("Total investments by Vineeta", shark_tank[shark_tank['Vineeta Investment Amount']>0][['Vineeta Investment Amount']].count().to_string()[-2:])
print("Investment amount by Vineeta", round(shark_tank['Vineeta Investment Amount'].sum()/100, 2), "crores")
print("Equity received by Vineeta", round(shark_tank['Vineeta Investment Equity'].sum(), 2), "% in different companies")
print("Debt/loan amount by Vineeta", round(shark_tank['Vineeta Debt Amount'].sum()/100, 2), "crores\n")

print("Company details:")
print('-'*75)
print(shark_tank.loc[shark_tank['Vineeta Investment Amount']>0][["Startup Name","Industry","Vineeta Investment Amount"]].to_string(index=False))
print('-'*75)

print("\nVineeta industry wise investments\n")
print(shark_tank[shark_tank['Vineeta Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False))
plt.figure(figsize = (10,6))
shark_tank[shark_tank['Vineeta Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.ylabel('')
plt.show()

tmpdf = shark_tank.loc[shark_tank['Vineeta Investment Amount']>0] [["Startup Name","Vineeta Investment Amount","Vineeta Investment Equity"]].sort_values(by="Vineeta Investment Equity")
fig = px.treemap(tmpdf, path=['Startup Name'], values=tmpdf['Vineeta Investment Amount'], width=800, height=800)
fig.update_layout(margin = dict(t=5, l=5, r=5, b=5))
fig.update_traces(textposition='middle center')
fig.show()

Total investments by Vineeta 43
Investment amount by Vineeta 11.71 crores
Equity received by Vineeta 242.3 % in different companies
Debt/loan amount by Vineeta 2.29 crores

Company details:
---------------------------------------------------------------------------
      Startup Name                     Industry  Vineeta Investment Amount
     BluePineFoods                         Food                      25.00
      BoozScooters Vehicles/Electrical Vehicles                      20.00
  HeartUpMySleeves               Beauty/Fashion                      12.50
              NOCD                         Food                      20.00
             CosIQ               Beauty/Fashion                      25.00
       JhaJiAchaar                         Food                      28.30
     SkippiIcePops                         Food                      20.00
        Get-A-Whey                         Food                      33.33
    TheQuirkyNaari               Beauty/Fashion                      17.50
SunfoxTechnologies               Medical/Health                      20.00
           HumpyA2                         Food                      33.33
 GoldSafeSolutions                Manufacturing                      16.66
        WakaoFoods                         Food                      25.00
       KabaddiAdda                       Sports                      40.00
  NomadFoodProject                         Food                      10.00
      JainShikanji                         Food                      10.00
          SneaKare               Beauty/Fashion                       7.00
             Dorji                         Food                      10.00
 WatchoutWearables                  Electronics                      50.00
             SoupX                         Food                      50.00
           Winston               Beauty/Fashion                      50.00
            TeaFit             Liquor/Beverages                      12.50
    TheSimplySalad                         Food                      15.00
          Paradyes               Beauty/Fashion                      32.50
            Snitch               Beauty/Fashion                      30.00
         HoneyVeda                         Food                      25.00
           PadCare                Manufacturing                      25.00
 SwadeshiBlessings         Furnishing/Household                      12.50
               OLL          Technology/Software                      15.00
            Geeani Vehicles/Electrical Vehicles                      33.33
     TheGreenSnack                         Food                     100.00
          Mahantam                Manufacturing                       6.00
         MindPeers               Medical/Health                      17.66
          Freakins               Beauty/Fashion                      50.00
           Perfora         Furnishing/Household                      26.66
           Trunome               Medical/Health                      37.50
  What'sUpWellness                         Food                      20.00
     HealthyMaster                         Food                      50.00
         NutriCook                         Food                      50.00
  ThePlatedProject                     Services                      25.00
            Rubans               Beauty/Fashion                      33.33
     ForeverModest               Beauty/Fashion                       5.00
        Naara-Aaba             Liquor/Beverages                      25.00
---------------------------------------------------------------------------

Vineeta industry wise investments

Industry
Food                            17
Beauty/Fashion                  10
Medical/Health                   3
Manufacturing                    3
Vehicles/Electrical Vehicles     2
Liquor/Beverages                 2
Furnishing/Household             2
Sports                           1
Electronics                      1
Technology/Software              1
Services                         1
Name: count, dtype: int64


print("Total investments by Aman", shark_tank[shark_tank['Aman Investment Amount']>=0][['Aman Investment Amount']].count().to_string()[-2:])
print("Investment amount by Aman", round(shark_tank['Aman Investment Amount'].sum()/100, 2), "crores")
print("Equity received by Aman", round(shark_tank['Aman Investment Equity'].sum(), 2), "% in different companies")
print("Debt/loan amount by Aman", round(shark_tank['Aman Debt Amount'].sum()/100, 2), "crores\n")

print("Company details:")
print('-'*75)
print(shark_tank.loc[shark_tank['Aman Investment Amount']>=0][["Startup Name","Industry","Aman Investment Amount"]].to_string(index=False))
print('-'*75)

print("\nAman industry wise investments\n")
print(shark_tank[shark_tank['Aman Investment Amount']>=0]['Industry'].value_counts().sort_values(ascending=False))
plt.figure(figsize = (10,6))
shark_tank[shark_tank['Aman Investment Amount']>=0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.ylabel('')
plt.show()

tmpdf = shark_tank.loc[shark_tank['Aman Investment Amount']>=0] [["Startup Name","Aman Investment Amount","Aman Investment Equity"]].sort_values(by="Aman Investment Equity")
fig = px.treemap(tmpdf, path=['Startup Name'], values=tmpdf['Aman Investment Amount'], width=800, height=800)
fig.update_layout(margin = dict(t=5, l=5, r=5, b=5))
fig.update_traces(textposition='middle center')
fig.show()

Total investments by Aman 73
Investment amount by Aman 25.1 crores
Equity received by Aman 260.93 % in different companies
Debt/loan amount by Aman 3.11 crores

Company details:
---------------------------------------------------------------------------
      Startup Name                     Industry  Aman Investment Amount
     BluePineFoods                         Food               25.000000
         Peeschute               Beauty/Fashion               75.000000
            Bummer               Beauty/Fashion               37.500000
        RevampMoto Vehicles/Electrical Vehicles               50.000000
     SkippiIcePops                         Food               20.000000
 RaisingSuperstars                    Education               50.000000
            Kavach                    Education                2.500000
       BeyondSnack                         Food               25.000000
             Altor                Manufacturing               25.000000
             Ariro                Manufacturing               25.000000
           Nuutjob               Beauty/Fashion                8.330000
          Meatyour                         Food               10.000000
         EventBeep                    Education               10.000000
             Farda               Beauty/Fashion               15.000000
              LOKA          Technology/Software               13.330000
     TheYarnBazaar                Manufacturing               25.000000
   TheRenalProject               Medical/Health               50.000000
   HammerLifestyle                  Electronics              100.000000
           Cocofit                         Food                0.000016
       BeyondWater                         Food               37.500000
          Let'sTry                         Food               22.500000
FindYourKicksIndia               Beauty/Fashion               10.000000
           WeSTOCK                  Animal/Pets               15.000000
            INACAN                         Food               20.000000
        Get-A-Whey                         Food               33.330000
       NamhyaFoods                         Food               50.000000
          AyuRythm               Medical/Health               75.000000
        GrowFitter          Technology/Software               50.000000
      JainShikanji                         Food               10.000000
          SneaKare               Beauty/Fashion                7.000000
             Hoovu                     Services               50.000000
    VeryMuchIndian               Beauty/Fashion               25.000000
             Stage                Entertainment               50.000000
    GearHeadMotors Vehicles/Electrical Vehicles               50.000000
            TeaFit             Liquor/Beverages               12.500000
        Haqdarshak                     Services               33.330000
    TheSimplySalad                         Food               15.000000
 AtypicalAdvantage          Technology/Software               15.000000
 HouseOfChikankari               Beauty/Fashion               37.500000
          Paradyes               Beauty/Fashion               32.500000
         Primebook          Technology/Software               37.500000
         GharSoaps               Beauty/Fashion               60.000000
         InsideFPV                Manufacturing               18.750000
        FastBeetle                     Services               45.000000
         Bullspree          Technology/Software               37.500000
            Snitch               Beauty/Fashion               30.000000
             Portl                     Services               50.000000
            Dabble                Manufacturing               15.000000
          Broomees                     Services               33.330000
            Geeani Vehicles/Electrical Vehicles               33.330000
          Manetain               Beauty/Fashion               75.000000
            UnStop          Technology/Software               50.000000
           BlueTea                         Food               50.000000
              Zoff                         Food              100.000000
          Mahantam                Manufacturing                6.000000
         MindPeers               Medical/Health               17.660000
         Daryaganj                         Food               90.000000
       DhruvVidyut Vehicles/Electrical Vehicles                0.000000
   TheHealthyBinge                         Food               25.000000
         MeduLance               Medical/Health               66.660000
          neuphony               Medical/Health               50.000000
            Malaki             Liquor/Beverages               25.000000
           nawgati                     Services               33.500000
           GladFul                         Food               16.660000
       Pharmallama               Medical/Health               40.000000
              Hood          Technology/Software               30.000000
           Trunome               Medical/Health               37.500000
             Wol3D                Manufacturing               80.000000
  What'sUpWellness                         Food               20.000000
  ThePlatedProject                     Services               25.000000
            Rubans               Beauty/Fashion               33.330000
         Sahayatha               Medical/Health               20.000000
               TAC               Beauty/Fashion               40.500000
---------------------------------------------------------------------------

Aman industry wise investments

Industry
Food                            18
Beauty/Fashion                  14
Medical/Health                   8
Manufacturing                    7
Technology/Software              7
Services                         7
Vehicles/Electrical Vehicles     4
Education                        3
Liquor/Beverages                 2
Electronics                      1
Animal/Pets                      1
Entertainment                    1
Name: count, dtype: int64


print("Total investments by Peyush", shark_tank[shark_tank['Peyush Investment Amount']>=0][['Peyush Investment Amount']].count().to_string()[-2:])
print("Investment amount by Peyush", round(shark_tank['Peyush Investment Amount'].sum()/100, 2), "crores")
print("Equity received by Peyush", round(shark_tank['Peyush Investment Equity'].sum(), 2), "% in different companies")
print("Debt/loan amount by Peyush", round(shark_tank['Peyush Debt Amount'].sum()/100, 2), "crores\n")

print("Company details:")
print('-'*75)
print(shark_tank.loc[shark_tank['Peyush Investment Amount']>=0][["Startup Name","Industry","Peyush Investment Amount"]].to_string(index=False))
print('-'*75)

print("\nPeyush industry wise investments\n")
print(shark_tank[shark_tank['Peyush Investment Amount']>=0]['Industry'].value_counts().sort_values(ascending=False))
plt.figure(figsize = (10,6))
shark_tank[shark_tank['Peyush Investment Amount']>=0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.ylabel('')
plt.show()

tmpdf = shark_tank.loc[shark_tank['Peyush Investment Amount']>=0] [["Startup Name","Peyush Investment Amount","Peyush Investment Equity"]].sort_values(by="Peyush Investment Equity")
fig = px.treemap(tmpdf, path=['Startup Name'], values=tmpdf['Peyush Investment Amount'], width=800, height=800)
fig.update_layout(margin = dict(t=5, l=5, r=5, b=5))
fig.update_traces(textposition='middle center')
fig.show()

Total investments by Peyush 69
Investment amount by Peyush 22.1 crores
Equity received by Peyush 422.11 % in different companies
Debt/loan amount by Peyush 3.42 crores

Company details:
---------------------------------------------------------------------------
      Startup Name                     Industry  Peyush Investment Amount
VivalyfInnovations               Medical/Health                 28.000000
             Ariro                Manufacturing                 25.000000
           Nuutjob               Beauty/Fashion                  8.330000
          Meatyour                         Food                 10.000000
         EventBeep                    Education                 10.000000
              LOKA          Technology/Software                 13.330000
             Annie                    Education                 35.000000
        Carragreen                Manufacturing                 25.000000
     TheYarnBazaar                Manufacturing                 25.000000
               PNT          Technology/Software                 25.000000
FindYourKicksIndia               Beauty/Fashion                 10.000000
      AasVidyalaya                    Education                 50.000000
        RoadBounce          Technology/Software                 80.000000
           WeSTOCK                  Animal/Pets                 15.000000
     TheStatePlate                         Food                 40.000000
            INACAN                         Food                 20.000000
      Sid07Designs                     Hardware                 25.000000
     HairOriginals               Beauty/Fashion                 20.000000
        KGAgrotech                  Agriculture                 10.000000
SunfoxTechnologies               Medical/Health                 20.000000
    IsakFragrances               Beauty/Fashion                 50.000000
 WattTechnovations               Medical/Health                  0.000253
 InsuranceSamadhan                     Services                100.000000
           HumpyA2                         Food                 33.330000
 GoldSafeSolutions                Manufacturing                 16.660000
         TweekLabs                       Sports                 20.000000
            Proxgy          Technology/Software                 50.000000
      StoreMyGoods                     Services                 25.000000
           WitBlox                Manufacturing                 30.000000
             Hoovu                     Services                 50.000000
             Dorji                         Food                 10.000000
             Stage                Entertainment                 50.000000
    GearHeadMotors Vehicles/Electrical Vehicles                 50.000000
         PatilKaki                         Food                 20.000000
            TeaFit             Liquor/Beverages                 12.500000
        Haqdarshak                     Services                 33.330000
 HouseOfChikankari               Beauty/Fashion                 37.500000
 ABCSports&Fitness                       Sports                 40.000000
         Primebook          Technology/Software                 37.500000
         InsideFPV                Manufacturing                 18.750000
             Kyari                Manufacturing                 25.500000
        FastBeetle                     Services                 45.000000
             Sepal                Manufacturing                 50.000000
           Solinas                     Services                 45.000000
            ekatra         Furnishing/Household                 10.000000
         NeoMotion                Manufacturing                100.000000
         Bullspree          Technology/Software                 37.500000
            Snitch               Beauty/Fashion                 30.000000
             Portl                     Services                 50.000000
          Broomees                     Services                 33.330000
           PadCare                Manufacturing                 25.000000
               OLL          Technology/Software                 15.000000
          Mahantam                Manufacturing                  6.000000
         MindPeers               Medical/Health                 53.000000
       DhruvVidyut Vehicles/Electrical Vehicles                  0.000000
             iMumz               Medical/Health                 10.000000
   TheHealthyBinge                         Food                 25.000000
           Perfora         Furnishing/Household                 26.660000
           CureSee               Medical/Health                 50.000000
         MeduLance               Medical/Health                 66.660000
          neuphony               Medical/Health                 50.000000
            Malaki             Liquor/Beverages                 25.000000
       Pharmallama               Medical/Health                 40.000000
              Hood          Technology/Software                 30.000000
            GROWiT                  Agriculture                 25.000000
           Trunome               Medical/Health                 37.500000
       SinghStyled               Beauty/Fashion                 50.000000
       LilGoodness                         Food                 50.000000
         Sahayatha               Medical/Health                 20.000000
---------------------------------------------------------------------------

Peyush industry wise investments

Industry
Medical/Health                  11
Manufacturing                   11
Food                             8
Technology/Software              8
Services                         8
Beauty/Fashion                   7
Education                        3
Agriculture                      2
Sports                           2
Vehicles/Electrical Vehicles     2
Liquor/Beverages                 2
Furnishing/Household             2
Animal/Pets                      1
Hardware                         1
Entertainment                    1
Name: count, dtype: int64


print("Total investments by Amit", shark_tank[shark_tank['Amit Investment Amount']>=0][['Amit Investment Amount']].count().to_string()[-2:])
print("Investment amount by Amit", round(shark_tank['Amit Investment Amount'].sum()/100, 2), "crores")
print("Equity received by Amit", round(shark_tank['Amit Investment Equity'].sum(), 2), "% in different companies")
print("Debt/loan amount by Amit", round(shark_tank['Amit Debt Amount'].sum()/100, 2), "crores\n")

print("Company details:")
print('-'*75)
print(shark_tank.loc[shark_tank['Amit Investment Amount']>0][["Startup Name","Industry","Amit Investment Amount"]].to_string(index=False))
print('-'*75)

print("\nAmit industry wise investments\n")
print(shark_tank[shark_tank['Amit Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False))
plt.figure(figsize = (10,6))
shark_tank[shark_tank['Amit Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.ylabel('')
plt.show()

tmpdf = shark_tank.loc[shark_tank['Amit Investment Amount']>0] [["Startup Name","Amit Investment Amount","Amit Investment Equity"]].sort_values(by="Amit Investment Equity")
fig = px.treemap(tmpdf, path=['Startup Name'], values=tmpdf['Amit Investment Amount'], width=800, height=800)
fig.update_layout(margin = dict(t=5, l=5, r=5, b=5))
fig.update_traces(textposition='middle center')
fig.show()

Total investments by Amit 21
Investment amount by Amit 8.01 crores
Equity received by Amit 109.0 % in different companies
Debt/loan amount by Amit 1.45 crores

Company details:
---------------------------------------------------------------------------
 Startup Name                     Industry  Amit Investment Amount
    InsideFPV                Manufacturing                   18.75
    Angrakhaa               Beauty/Fashion                   40.00
    MoppFoods                         Food                   75.00
       Dobiee                         Food                   72.00
        Pflow               Medical/Health                   30.00
       ekatra         Furnishing/Household                   10.00
    licksters                         Food                   25.00
   ScrapUncle                     Services                   60.00
       UnStop          Technology/Software                   50.00
  Cakelicious                         Food                   25.00
     Hornback Vehicles/Electrical Vehicles                   50.00
      nawgati                     Services                   33.50
      GladFul                         Food                   16.66
  Pharmallama               Medical/Health                   40.00
      funngro          Technology/Software                   25.00
       Aadvik                         Food                   15.00
ForeverModest               Beauty/Fashion                    5.00
    Sahayatha               Medical/Health                   20.00
       maisha               Beauty/Fashion                   10.00
     NishHair               Beauty/Fashion                  100.00
     StyloBug               Beauty/Fashion                   80.00
---------------------------------------------------------------------------

Amit industry wise investments

Industry
Food                            6
Beauty/Fashion                  5
Medical/Health                  3
Services                        2
Technology/Software             2
Manufacturing                   1
Furnishing/Household            1
Vehicles/Electrical Vehicles    1
Name: count, dtype: int64


print("Total investments by all Guests", shark_tank[shark_tank['Guest Investment Amount']>=0][['Guest Investment Amount']].count().to_string()[-2:])
print("Investment amount by all Guests", round(shark_tank['Guest Investment Amount'].sum()/100, 2), "crores")
print("Equity received by all Guests", round(shark_tank['Guest Investment Equity'].sum(), 2), "% in different companies")
print("Debt/loan amount by all Guests", round(shark_tank['Guest Debt Amount'].sum()/100, 2), "crores\n")

print("Company details:")
print('-'*75)
print(shark_tank.loc[shark_tank['Guest Investment Amount']>0][["Startup Name","Industry","Guest Investment Amount"]].to_string(index=False))
print('-'*75)

print("\nAll Guests industry wise investments\n")
print(shark_tank[shark_tank['Guest Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False))
plt.figure(figsize = (10,6))
shark_tank[shark_tank['Guest Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.ylabel('')
plt.show()

tmpdf = shark_tank.loc[shark_tank['Guest Investment Amount']>0] [["Startup Name","Guest Investment Amount","Guest Investment Equity"]].sort_values(by="Guest Investment Equity")
fig = px.treemap(tmpdf, path=['Startup Name'], values=tmpdf['Guest Investment Amount'], width=800, height=800)
fig.update_layout(margin = dict(t=5, l=5, r=5, b=5))
fig.update_traces(textposition='middle center')
fig.show()

Total investments by all Guests 37
Investment amount by all Guests 14.22 crores
Equity received by all Guests 123.7 % in different companies
Debt/loan amount by all Guests 1.95 crores

Company details:
---------------------------------------------------------------------------
        Startup Name                     Industry  Guest Investment Amount
          TheSassBar               Beauty/Fashion                25.000000
  SunfoxTechnologies               Medical/Health                20.000000
   WattTechnovations               Medical/Health                 0.000253
             HumpyA2                         Food                33.330000
   GoldSafeSolutions                Manufacturing                16.660000
          WakaoFoods                         Food                25.000000
    NomadFoodProject                         Food                10.000000
             WitBlox                Manufacturing                30.000000
                 TAC               Beauty/Fashion                40.500000
          Naara-Aaba             Liquor/Beverages                25.000000
              RodBez Vehicles/Electrical Vehicles                10.000000
                Blix          Technology/Software                40.000000
               TURMS               Beauty/Fashion               120.000000
             mintree               Beauty/Fashion                45.000000
            DilFoods                         Food               100.000000
         GoenchiFeni             Liquor/Beverages               200.000000
              GudGum                         Food                20.000000
            EvaScalp               Medical/Health                10.000000
          HoneyTwigs                         Food                25.000000
            JewelBox               Beauty/Fashion                80.000000
            DaakRoom                     Services                36.000000
         NasherMiles               Beauty/Fashion                60.000000
             Without               Beauty/Fashion                37.500000
                Kibo          Technology/Software                30.000000
            YesMadam               Beauty/Fashion                37.500000
ToffeeCoffeeRoasters             Liquor/Beverages                35.000000
            Chefling                         Food                10.000000
             ToHands          Technology/Software                60.000000
            PlusGold          Technology/Software                60.000000
             Aroleap               Medical/Health                25.000000
            WiseLife               Medical/Health                30.000000
          ModelVerse          Technology/Software                 8.330000
              MEPACK                     Services                 3.500000
               Deeva               Beauty/Fashion                50.000000
                Sama                     Services                33.330000
           Dharaksha                Manufacturing                 0.002500
        iDreamCareer          Technology/Software                30.000000
---------------------------------------------------------------------------

All Guests industry wise investments

Industry
Beauty/Fashion                  9
Food                            7
Technology/Software             6
Medical/Health                  5
Manufacturing                   3
Liquor/Beverages                3
Services                        3
Vehicles/Electrical Vehicles    1
Name: count, dtype: int64


# Guest sharks and number of companies they invested
shark_tank.loc[shark_tank['Guest Investment Amount'] > 1]['Invested Guest Name'].str.split(',').explode('Guest Name').value_counts().sort_values(ascending=False)

Invested Guest Name
Ritesh Aggarwal     17
Ghazal Alagh         7
Azhar Iqubal         4
Radhika Gupta        4
Vikas D Nahar        2
Ronnie Screwvala     2
Varun Dua            2
Deepinder Goyal      1
Name: count, dtype: int64


# Investment amount by guests, in lakhs
round(shark_tank.groupby(["Invested Guest Name"])["Guest Investment Amount"].sum().sort_values(ascending=False))

Invested Guest Name
Ritesh Aggarwal                  379.0
Ritesh Aggarwal,Radhika Gupta    230.0
Azhar Iqubal                     200.0
Deepinder Goyal                  200.0
Ghazal Alagh                     160.0
Ronnie Screwvala                  68.0
Vikas D Nahar                     66.0
Varun Dua                         60.0
Varun Dua,Radhika Gupta           60.0
Name: Guest Investment Amount, dtype: float64


# tmpdf = shark_tank.loc[shark_tank['Guest Investment Amount'] > 1]
# tmpdf[['Invested Guest Name','Guest Investment Amount']]


# Number of sharks in a deal, in all seasons
print(shark_tank['Number of Sharks in Deal'].value_counts(), "\n")

# In percentage
print(round(shark_tank['Number of Sharks in Deal'].value_counts(normalize=True)*100).astype(str).str.replace('.0', '%', regex=False))

fig = plt.figure(figsize=(8, 6))
plt.title("Number of sharks in a deal, in all seasons", fontsize=15)
plt.xticks(fontsize=15)
plt.yticks([])
ax = sns.countplot(data = shark_tank, x = 'Number of Sharks in Deal')
ax.set_ylabel('')
for t in ax.patches:
    if (np.isnan(float(t.get_height()))):
        ax.annotate(0, (t.get_x(), 0))
    else:
        ax.annotate(str(format(int(t.get_height()), ',d')), (t.get_x(), t.get_height()*1.01), size=14)

Number of Sharks in Deal
1.0    107
2.0     75
3.0     36
4.0     18
5.0     12
Name: count, dtype: int64 

Number of Sharks in Deal
1.0    43%
2.0    30%
3.0    15%
4.0     7%
5.0     5%
Name: proportion, dtype: object


# All sharks deals
print(shark_tank.loc[shark_tank['Number of Sharks in Deal'] >= 5][["Season Number","Startup Name","Total Deal Amount","Total Deal Equity"]])

     Season Number        Startup Name  Total Deal Amount  Total Deal Equity
15               1       SkippiIcePops           100.0000               15.0
50               1  FindYourKicksIndia            50.0000               25.0
64               1              INACAN           100.0000               10.0
80               1  SunfoxTechnologies           100.0000                6.0
209              2              Snitch           150.0000                1.5
239              2            Mahantam            30.0000               20.0
274              2         Pharmallama           200.0000                5.0
311              2           Sahayatha           100.0000               10.0
357              3            JewelBox           200.0000                6.0
365              3         NasherMiles           300.0000                1.5
423              3           LittleBox            75.0000                2.5
435              3           Dharaksha             0.0125                1.0


# Sharks with most number of solo deals
amt_cols = shark_tank.columns[shark_tank.columns.str.contains(' Investment Amount')].tolist()
tmp = shark_tank.loc[shark_tank['Number of Sharks in Deal'] == 1][amt_cols]
tmp.count().sort_values(ascending=False).nlargest(3)

# Namita did most number of solo deals, than any other Shark

Namita Investment Amount    24
Aman Investment Amount      21
Peyush Investment Amount    17
dtype: int64


# Sharks with most number of episode presence, in all seasons
present_cols = shark_tank.columns[shark_tank.columns.str.endswith(' Present')].tolist()
tmp = shark_tank[present_cols]
tmp.sum().sort_values(ascending=False).nlargest(3)

# Anupam was there in most number of episodes, in 3 seasons

Anupam Present    390.0
Aman Present      383.0
Namita Present    361.0
dtype: float64


# Sharks with most number of episode presence, in current/latest season (3rd Season)
tmp = shark_tank.loc[shark_tank['Season Number'] == 3][present_cols]
tmp.sum().sort_values(ascending=False).nlargest(4)

Aman Present       111.0
Anupam Present     108.0
Guest Present      102.0
Vineeta Present     96.0
dtype: float64


# Anchor and number of pitches, they hosted
shark_tank.groupby('Anchor').size()

Anchor
Rahul Dua          289
Rannvijay Singh    152
dtype: int64


# Anchor and number of episodes, they hosted
pd.pivot_table(shark_tank, values='Episode Number', columns='Anchor', aggfunc='max')


# Sharks
# tmp = shark_tank.loc[shark_tank['Number of Sharks in Deal'] == 2][amt_cols].stack()
# tmp
# tmp2 = shark_tank.loc[shark_tank['Number of Sharks in Deal'] == 2][amt_cols].transpose()
# tmp2


print(shark_tank['Pitchers State'].str.split(',').explode('Pitchers State').value_counts(), "\n")
shark_tank['Pitchers State'].str.split(',').explode('Pitchers State').value_counts().sort_values().plot.barh()

Pitchers State
Maharashtra          127
Delhi                 67
Karnataka             52
Gujarat               46
Haryana               30
Uttar Pradesh         24
West Bengal           19
Rajasthan             17
Telangana             16
Punjab                12
Tamil Nadu             9
Madhya Pradesh         8
Bihar                  5
Goa                    4
Jammu & Kashmir        4
Kerala                 4
Uttarakhand            3
Jharkhand              3
Himachal Pradesh       2
Chhattisgarh           2
Assam                  2
Andhra Pradesh         1
Arunachal Pradesh      1
Tamilnadu              1
 Bihar                 1
 Kerala                1
Name: count, dtype: int64

<Axes: ylabel='Pitchers State'>


# Top 20 Indian Cities
tmp = shark_tank['Pitchers City'].value_counts().nlargest(20).sort_values(ascending=True)
fig = px.bar(tmp, x=tmp.values, title="<b>Indian top 20 cities</b> with number of startups came for pitching", template='simple_white', text=tmp, width=850, height=800)
fig.update_yaxes(title_text="")
fig.update_xaxes(visible=False)
fig.show()


# Most frequently asked amount, by startups
shark_tank.groupby('Original Ask Amount').size().nlargest(10)

# Original Ask Amount (in lakhs) and Number of times asked

Original Ask Amount
50.0     94
100.0    77
75.0     52
60.0     27
40.0     22
80.0     22
150.0    20
30.0     19
90.0     11
200.0    11
dtype: int64


# Most frequently offered equity, by startups
shark_tank.groupby('Original Offered Equity').size().nlargest(10)

# Original Offered Equity (in %) and Number of times offered

Original Offered Equity
1.0     89
2.0     67
5.0     67
10.0    40
3.0     37
4.0     26
2.5     25
0.5     17
1.5     15
7.5     11
dtype: int64


# ✅ Most frequently invested amount, by Sharks
shark_tank.groupby('Total Deal Amount').size().nlargest(10)

# Sharks mostly invested between 50K-1lakh per deal

# Total Deal Amount (in lakhs) and Number of times invested

Total Deal Amount
50.0     47
100.0    39
75.0     24
60.0     20
40.0     14
30.0     13
25.0     12
20.0      9
80.0      9
10.0      7
dtype: int64


# shark_tank.groupby(['Original Ask Amount','Received Offer']).size().nlargest(100)


# ✅ Most frequently received total equity, by Sharks
shark_tank.groupby('Total Deal Equity').size().nlargest(10)

# Sharks are expecting around 10-20% equity, in a deal

# Total Deal Equity (in %) and Number of times invested

Total Deal Equity
10.0    30
1.0     23
5.0     23
2.0     17
4.0     17
3.0     16
20.0    16
15.0    14
6.0     13
2.5      9
dtype: int64


# ✅ Mostly successful combinations (of asked amount and offered equity)
shark_tank.loc[shark_tank['Received Offer'] == 1].groupby(['Original Ask Amount','Original Offered Equity']).size().nlargest(10)

Original Ask Amount  Original Offered Equity
50.0                 5.0                        16
100.0                1.0                        15
50.0                 2.0                        10
100.0                2.0                        10
60.0                 2.0                         8
50.0                 1.0                         7
                     3.0                         7
75.0                 4.0                         7
50.0                 10.0                        6
70.0                 1.0                         6
dtype: int64


# Most frequently asked amount, by startups who could NOT get a deal
shark_tank.loc[shark_tank['Received Offer'] == 0].groupby('Original Ask Amount').size().nlargest(10)

# Original Ask Amount (in lakhs) and Number of times asked (but rejected by sharks) ❌

Original Ask Amount
50.0     30
100.0    23
75.0     16
60.0      9
80.0      8
40.0      6
90.0      6
200.0     6
20.0      4
25.0      4
dtype: int64


# Most frequently offered equity, by startups who could NOT get a deal
shark_tank.loc[shark_tank['Received Offer'] == 0].groupby('Original Offered Equity').size().nlargest(10)

# Original Offered Equity (in %) and Number of times offered (but rejected by sharks) ❌

Original Offered Equity
5.0     29
1.0     24
2.0     17
10.0    14
3.0     12
2.5     11
1.5      4
4.0      4
7.5      4
15.0     4
dtype: int64


# Mostly rejected combinations (of asked amount and offered equity)
shark_tank.loc[shark_tank['Received Offer'] == 0].groupby(['Original Ask Amount','Original Offered Equity']).size().nlargest(5)

# You may not get deal, if you ask for 1 crore with 1% equity or 50K with 5%/10% equity 🔴

Original Ask Amount  Original Offered Equity
100.0                1.0                        9
50.0                 5.0                        8
                     10.0                       5
75.0                 5.0                        5
60.0                 2.0                        4
dtype: int64


shp_gdf = gpd.read_file('../input/india-gis-data/India States/Indian_states.shp')
merged = shp_gdf.set_index('st_nm').join(shark_tank.set_index('Pitchers State'))
merged['Total Deal Amount'] = merged['Total Deal Amount'].fillna(0)

fig, ax = plt.subplots(1, figsize=(12, 12))
ax.axis('off')
ax.set_title('As per Total Deal Amount', fontdict={'fontsize': '15', 'fontweight' : '3'})
fig = merged.plot(column='Total Deal Amount', cmap='YlGn', linewidth=0.8, ax=ax, edgecolor='0.5', legend=True)


# All season's, startup companies incorporated year
fig = plt.figure(figsize=(10, 7))
plt.title('Number of companies and Year of establishment of startups', size=14)
tmp = shark_tank.loc[shark_tank['Started in'].notnull()]
ax = sns.countplot(data = tmp, x = 'Started in')
ax.set_xlabel('Started in', fontsize=13)
plt.yticks([])
ax.set_ylabel('')
for t in ax.patches:
    if (np.isnan(float(t.get_height()))):
        ax.annotate(0, (t.get_x(), 0))
    else:
        ax.annotate(str(format(int(t.get_height()), ',d')), (t.get_x(), t.get_height()*1.02), size=14)


# Some companies got more amount than they asked/expected
print(shark_tank.loc[shark_tank['Original Ask Amount'] < shark_tank["Total Deal Amount"]][["Startup Name"]].count())
shark_tank.loc[shark_tank['Original Ask Amount'] < shark_tank["Total Deal Amount"]][["Season Number","Startup Name","Original Ask Amount","Total Deal Amount"]]

Startup Name    35
dtype: int64


# Most of the companies diluted/gave their company equity more than they initially offered/expected
shark_tank.loc[shark_tank['Original Offered Equity'] < shark_tank["Total Deal Equity"]][["Season Number","Startup Name","Original Offered Equity","Total Deal Equity"]]


# Below (19) companies got the same valuation they requested (with or without loan)
print(shark_tank.loc[shark_tank['Valuation Requested'] == shark_tank["Deal Valuation"]][["Startup Name"]].count())
shark_tank.loc[shark_tank['Valuation Requested'] == shark_tank["Deal Valuation"]][["Season Number","Startup Name","Valuation Requested","Deal Valuation"]]

Startup Name    28
dtype: int64


# There is 1 company which got more valuation than they pitched, JhaJi Achaar received after the Season (in 2023)
shark_tank.loc[shark_tank['Valuation Requested'] < shark_tank["Deal Valuation"]][["Startup Name","Valuation Requested","Deal Valuation"]]


# Some companies were on pre-revenue or didn't had any revenue (as of pitching day)
shark_tank.loc[shark_tank['Yearly Revenue'] == 0]


# Some companies were on burning/paying money from their pocket, without any profit (as of pitching day)
shark_tank.loc[shark_tank['Cash Burn'] == 'yes']


# Top 15 Highest Yearly Revenue brands, in all seasons
print(shark_tank.groupby('Startup Name')['Yearly Revenue'].max().nlargest(15))

tmpdf = shark_tank.sort_values('Yearly Revenue', ascending=False)[0:15]
fig = px.bar(tmpdf, x="Startup Name", y='Yearly Revenue', color="Startup Name", template='simple_white', title="<b>Highest revenue (in lakhs) of participated startups, in all seasons</b>", text=tmpdf['Yearly Revenue'])
fig.show()

Startup Name
FrenchCrown          7200.0
Rubans               5100.0
Toyshine             4500.0
GuardianGears        2500.0
GunjanAppsStudios    2400.0
UnStop               1600.0
StyloBug             1400.0
RaisingSuperstars    1300.0
DesmondJi            1200.0
Eume                 1200.0
PlayBoxTV            1020.0
oyehappy             1005.0
Alpino               1000.0
BlueTea              1000.0
HammerLifestyle      1000.0
Name: Yearly Revenue, dtype: float64


# Top 10 Highest Yearly Revenue brands, in latest/current season (3rd season)
print(shark_tank_season3.groupby('Startup Name')['Yearly Revenue'].max().nlargest(10))

tmpdf = shark_tank_season3.sort_values('Yearly Revenue', ascending=False)[0:10]
fig = px.bar(tmpdf, x="Startup Name", y='Yearly Revenue', color="Startup Name", template='simple_white', title="<b>Highest revenue (in lakhs) of participated startups, in Season 3</b>", text=tmpdf['Yearly Revenue'])
fig.show()

Startup Name
Refit                  18700
NasherMiles             5700
YesMadam                5000
BaccaBucci              4700
LittleBox               3600
Zorko                   3000
AdilQadri               2070
UrbanSpace              2050
HonestHome              1400
UnclePetersPanCakes     1400
Name: Yearly Revenue, dtype: Int32


# Filter data for the 3rd season
season3_data = df[df['Season Number'] == 3]


# Top 15 highest Gross Margin brands, in all seasons
print(shark_tank.groupby('Startup Name')['Gross Margin'].max().nlargest(15))

tmpdf = shark_tank.sort_values('Gross Margin', ascending=False)[0:15]
fig = px.bar(tmpdf, x="Startup Name", y='Gross Margin', color="Startup Name", template='simple_white', title="<b>Highest Gross margin (in %) of the brands (in all seasons)</b>", text=tmpdf['Gross Margin'].map(int).map(str) + "%")
fig.show()

Startup Name
Poo-de-Cologne        150.0
Farda                 115.0
Cocofit                95.0
UnStop                 90.0
MidNightAngelsByPC     83.0
Auli                   80.0
LeafyAffair            80.0
Pflow                  80.0
ekatra                 80.0
oyehappy               80.0
CosIQ                  75.0
Dabble                 75.0
JaipurWatchCompany     75.0
TheaandSid             75.0
Bummer                 70.0
Name: Gross Margin, dtype: float64


# Top 15 highest Net Margin brands, in all seasons
print(shark_tank.groupby('Startup Name')['Net Margin'].max().nlargest(15))

tmpdf = shark_tank.sort_values('Net Margin', ascending=False)[0:15]
fig = px.bar(tmpdf, x="Startup Name", y='Net Margin', color="Startup Name", template='simple_white', title="<b>Highest Net margin (in %) of the brands</b>", text=tmpdf['Net Margin'].map(int).map(str) + "%")
fig.show()

Startup Name
Cakelicious           40.0
TwistingScoops        40.0
SharmaJiKiAata        38.0
DrCubes               35.0
Pabiben               35.0
VAPerfume             35.0
NishHair              30.0
UpThrust              30.0
ekatra                28.0
Tipayi                26.0
Flhexible             25.0
MeduLance             24.0
eyenic                21.0
Febris                20.0
MidNightAngelsByPC    20.0
Name: Net Margin, dtype: float64


# Word cloud based on Business Description of startups came in all seasons
text = " Shark Tank India ".join(cat for cat in shark_tank.loc[shark_tank['Business Description'].notnull()]['Business Description'])
stop_words = list(STOPWORDS)
wordcloud = WordCloud(width=2000, height=1500, stopwords=stop_words, background_color='salmon', colormap='Pastel1', collocations=False, random_state=2024).generate(text)
plt.figure(figsize=(20,20))
plt.imshow(wordcloud)
plt.axis("off")
plt.show()


# Word cloud based on Business Description, startups came in current/latest season (3rd season)
text = " Shark Tank India ".join(cat for cat in shark_tank_season3.loc[shark_tank_season3['Business Description'].notnull()]['Business Description'])
stop_words = list(STOPWORDS)
wordcloud = WordCloud(width=2000, height=1500, stopwords=stop_words, background_color='salmon', colormap='Pastel2', collocations=False, random_state=2024).generate(text)
plt.figure(figsize=(20,16))
plt.imshow(wordcloud)
plt.axis("off")
plt.show()


# Correlation matrix
shark_tank.corr(numeric_only=True).style.background_gradient(cmap = 'Blues')


print("numpy version: {}". format(np.__version__))
print("pandas version: {}". format(pd.__version__))
import matplotlib
print("matplotlib version: {}". format(matplotlib. __version__))
print("seaborn version: {}". format(sns.__version__))
import plotly
print("plotly version: {}". format(plotly.__version__))

# Current Python package versions
# numpy version: 1.26.4
# pandas version: 2.2.0
# matplotlib version: 3.7.5
# seaborn version: 0.12.2
# plotly version: 5.18.0

numpy version: 1.26.4
pandas version: 2.2.0
matplotlib version: 3.7.5
seaborn version: 0.12.2
plotly version: 5.18.0


shark_tank.loc[shark_tank['Number of Presenters'] != shark_tank['Male Presenters'].fillna(0) + shark_tank['Female Presenters'] + shark_tank['Transgender Presenters'].fillna(0)]


shark_tank.loc[(shark_tank['Male Presenters'].isnull()) & (shark_tank['Couple Presenters'] == 1)]


shark_tank.loc[(shark_tank['Female Presenters'].isnull()) & (shark_tank['Couple Presenters'] == 1)]


shark_tank.loc[(shark_tank['Accepted Offer'] == 1) & (shark_tank['Total Deal Amount'].isnull())]


shark_tank.loc[(shark_tank['Accepted Offer'] == 1) & (shark_tank['Number of Sharks in Deal'].isnull())]


shark_tank.loc[(shark_tank['Accepted Offer'].isnull()) & (shark_tank['Number of Sharks in Deal'] >= 1)]


shark_tank.loc[round(shark_tank['Total Deal Amount'].fillna(0),1) != round(shark_tank['Ashneer Investment Amount'].fillna(0) + shark_tank['Namita Investment Amount'].fillna(0) + shark_tank['Anupam Investment Amount'].fillna(0) + shark_tank['Vineeta Investment Amount'].fillna(0) + shark_tank['Aman Investment Amount'].fillna(0) + shark_tank['Peyush Investment Amount'].fillna(0) + shark_tank['Amit Investment Amount'].fillna(0) + shark_tank['Guest Investment Amount'].fillna(0), 1)]


shark_tank.loc[round(shark_tank['Total Deal Equity'].fillna(0),1) != round(shark_tank['Ashneer Investment Equity'].fillna(0) + shark_tank['Namita Investment Equity'].fillna(0) + shark_tank['Anupam Investment Equity'].fillna(0) + shark_tank['Vineeta Investment Equity'].fillna(0) + shark_tank['Aman Investment Equity'].fillna(0) + shark_tank['Peyush Investment Equity'].fillna(0) + shark_tank['Amit Investment Equity'].fillna(0) + shark_tank['Guest Investment Equity'].fillna(0),1)]


shark_tank.loc[round(shark_tank['Total Deal Debt'].fillna(0),1) != round(shark_tank['Ashneer Debt Amount'].fillna(0) + shark_tank['Namita Debt Amount'].fillna(0) + shark_tank['Anupam Debt Amount'].fillna(0) + shark_tank['Vineeta Debt Amount'].fillna(0) + shark_tank['Aman Debt Amount'].fillna(0) + shark_tank['Peyush Debt Amount'].fillna(0) + shark_tank['Amit Debt Amount'].fillna(0) + shark_tank['Guest Debt Amount'].fillna(0),1)]


shark_tank.loc[(shark_tank['Received Offer'] == 1) & (shark_tank['Accepted Offer'].isnull())]


shark_tank.loc[(shark_tank['Received Offer'] == 0) & (shark_tank['Accepted Offer'].notnull())]


shark_tank.loc[(shark_tank['Number of Sharks in Deal'].fillna(0).round(0).astype(int) != shark_tank['Ashneer Investment Amount'].notnull().astype("int") + shark_tank['Namita Investment Amount'].notnull().astype("int") + shark_tank['Anupam Investment Amount'].notnull().astype("int") + shark_tank['Vineeta Investment Amount'].notnull().astype("int") + shark_tank['Aman Investment Amount'].notnull().astype("int") + shark_tank['Peyush Investment Amount'].notnull().astype("int") + shark_tank['Amit Investment Amount'].notnull().astype("int") + shark_tank['Guest Investment Amount'].notnull().astype("int")) & (shark_tank['Guest Present']<2) ]


shark_tank.loc[(shark_tank['Couple Presenters'] != 0) & (shark_tank['Couple Presenters'] != 1)]


shark_tank.loc[(shark_tank['Received Offer'] != 0) & (shark_tank['Received Offer'] != 1)]


shark_tank.loc[(shark_tank['Accepted Offer'] != 0) & (shark_tank['Accepted Offer'] != 1)]


shark_tank.loc[(shark_tank['Ashneer Investment Amount'].notnull()) & (shark_tank['Ashneer Present'] != 1)]


shark_tank.loc[(shark_tank['Namita Investment Amount'].notnull()) & (shark_tank['Namita Present'] != 1)]


shark_tank.loc[(shark_tank['Anupam Investment Amount'].notnull()) & (shark_tank['Anupam Present'] != 1)]


shark_tank.loc[(shark_tank['Vineeta Investment Amount'].notnull()) & (shark_tank['Vineeta Present'] != 1)]


shark_tank.loc[(shark_tank['Aman Investment Amount'].notnull()) & (shark_tank['Aman Present'] != 1)]


shark_tank.loc[(shark_tank['Peyush Investment Amount'].notnull()) & (shark_tank['Peyush Present'] != 1)]


shark_tank.loc[(shark_tank['Amit Investment Amount'].notnull()) & (shark_tank['Amit Present'] != 1)]


shark_tank.loc[(shark_tank['Guest Investment Amount'].notnull()) & (shark_tank['Guest Present'].isnull())]


shark_tank.loc[(shark_tank['Guest Investment Amount'].notnull()) & (shark_tank['Invested Guest Name'].isnull())]


shark_tank.loc[(shark_tank['All Guest Names'].isnull()) & (shark_tank['Guest Present'].notnull())]


shark_tank.loc[(shark_tank['Total Deal Debt'].isnull()) & (shark_tank['Debt Interest'].notnull())]


shark_tank.loc[(shark_tank['Received Offer'] == 0) & (shark_tank['Deal Has Conditions'].notnull())]


shark_tank.loc[(shark_tank['Accepted Offer'] == 0) & (shark_tank['Royalty Deal'].notnull())]


shark_tank.loc[(shark_tank['Accepted Offer'] == 0) & (shark_tank['Advisory Shares Equity'].notnull())]

	Season Number	Startup Name	Episode Number	Pitch Number	Season Start	Season End	Original Air Date	Episode Title	Anchor	Industry	Business Description	Company Website	Started in	Number of Presenters	Male Presenters	Female Presenters	Transgender Presenters	Couple Presenters	Pitchers Average Age	Pitchers City	Pitchers State	Yearly Revenue	Monthly Sales	Gross Margin	Net Margin	...	Aman Investment Amount	Aman Investment Equity	Aman Debt Amount	Peyush Investment Amount	Peyush Investment Equity	Peyush Debt Amount	Amit Investment Amount	Amit Investment Equity	Amit Debt Amount	Ashneer Investment Amount	Ashneer Investment Equity	Ashneer Debt Amount	Guest Investment Amount	Guest Investment Equity	Guest Debt Amount	Invested Guest Name	All Guest Names	Namita Present	Vineeta Present	Anupam Present	Aman Present	Peyush Present	Amit Present	Ashneer Present	Guest Present
0	1	BluePineFoods	1	1	20-Dec-21	4-Feb-22	20-Dec-21	Badlegi Business Ki Tasveer	Rannvijay Singh	Food	Frozen Momos	https://bluepinefoods.com/	2016.0	3	2.0	1.0	NaN	0.0	Middle	Delhi	Delhi	95.0	8.0	NaN	NaN	...	25.0	5.33	NaN	NaN	NaN	NaN	NaN	NaN	NaN	25.0	5.33	NaN	NaN	NaN	NaN	NaN	NaN	1.0	1.0	1.0	1.0	NaN	NaN	1.0	NaN
1	1	BoozScooters	1	2	20-Dec-21	4-Feb-22	20-Dec-21	Badlegi Business Ki Tasveer	Rannvijay Singh	Vehicles/Electrical Vehicles	Renting e-bike for mobility in private spaces	https://www.boozup.net/	2017.0	1	1.0	NaN	NaN	0.0	Young	Ahmedabad	Gujarat	4.0	0.4	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	20.0	25.00	NaN	NaN	NaN	NaN	NaN	NaN	1.0	1.0	1.0	1.0	NaN	NaN	1.0	NaN
2	1	HeartUpMySleeves	1	3	20-Dec-21	4-Feb-22	20-Dec-21	Badlegi Business Ki Tasveer	Rannvijay Singh	Beauty/Fashion	Detachable Sleeves	https://heartupmysleeves.com/	2021.0	1	NaN	1.0	NaN	0.0	Young	Delhi	Delhi	NaN	2.0	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	1.0	1.0	1.0	NaN	NaN	1.0	NaN
3	1	TagzFoods	2	4	20-Dec-21	4-Feb-22	21-Dec-21	Insaan, Ideas Aur Sapne	Rannvijay Singh	Food	Healthy Potato Chips Snacks	https://tagzfoods.com/	2019.0	2	2.0	NaN	NaN	0.0	Middle	Bangalore	Karnataka	700.0	NaN	48.0	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	70.0	2.75	NaN	NaN	NaN	NaN	NaN	NaN	1.0	1.0	1.0	1.0	NaN	NaN	1.0	NaN
4	1	HeadAndHeart	2	5	20-Dec-21	4-Feb-22	21-Dec-21	Insaan, Ideas Aur Sapne	Rannvijay Singh	Education	Brain Development Course	https://thehnh.in/	2015.0	4	1.0	3.0	NaN	1.0	Middle	Patiala	Punjab	30.0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	1.0	1.0	1.0	NaN	NaN	1.0	NaN
5	1	Agritourism	2	6	20-Dec-21	4-Feb-22	21-Dec-21	Insaan, Ideas Aur Sapne	Rannvijay Singh	Agriculture	Tourism	https://www.agritourism.in/	2005.0	2	1.0	1.0	NaN	1.0	Middle	Baramati	Maharashtra	79.0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	1.0	1.0	1.0	NaN	NaN	1.0	NaN
6	1	qZenseLabs	3	7	20-Dec-21	4-Feb-22	22-Dec-21	Aam Aadmi Ke Business Ideas	Rannvijay Singh	Food	Food Freshness Detector	https://www.qzense.com/	2020.0	2	NaN	2.0	NaN	0.0	Middle	Delhi,Mohali	Delhi,Punjab	25.0	15.0	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	1.0	1.0	1.0	NaN	NaN	1.0	NaN
7	1	Peeschute	3	8	20-Dec-21	4-Feb-22	22-Dec-21	Aam Aadmi Ke Business Ideas	Rannvijay Singh	Beauty/Fashion	Disposable Urine Bag	https://www.peeschute.com/	2019.0	1	1.0	NaN	NaN	0.0	Young	Jalna	Maharashtra	100.0	NaN	NaN	NaN	...	75.0	6.00	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	1.0	1.0	1.0	NaN	NaN	1.0	NaN

	431	432	433	434	435	436	437	438	439	440
Season Number	3	3	3	3	3	3	3	3	3	3
Startup Name	Myracle.io	Cup-ji	AToddlerThing	FlexifyMe	Dharaksha	iDreamCareer	RockPaperRum	Fit&Flex	Sukham	Smotect
Episode Number	37	38	38	38	39	39	39	40	40	40
Pitch Number	432	433	434	435	436	437	438	439	440	441
Season Start	22-Jan-24	22-Jan-24	22-Jan-24	22-Jan-24	22-Jan-24	22-Jan-24	22-Jan-24	22-Jan-24	22-Jan-24	22-Jan-24
...	...	...	...	...	...	...	...	...	...	...
Aman Present	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
Peyush Present	NaN	NaN	NaN	NaN	1.0	1.0	1.0	NaN	NaN	NaN
Amit Present	1.0	1.0	1.0	1.0	NaN	NaN	NaN	NaN	NaN	NaN
Ashneer Present	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Guest Present	1.0	NaN	NaN	NaN	1.0	1.0	1.0	1.0	1.0	1.0

	count	mean	std	min	25%	50%	75%	max
Season Number	441.000000	1.927438	0.782882	1.000000	1.000000	2.000000	3.000000	3.000000
Episode Number	441.000000	21.215420	13.919291	0.000000	9.000000	21.000000	32.000000	51.000000
Pitch Number	441.000000	221.000000	127.449990	1.000000	111.000000	221.000000	331.000000	441.000000
Started in	321.000000	2018.791277	2.814591	1998.000000	2018.000000	2019.000000	2021.000000	2023.000000
Number of Presenters	441.000000	2.029478	0.829316	1.000000	1.000000	2.000000	2.000000	6.000000
Male Presenters	381.000000	1.695538	0.831426	1.000000	1.000000	2.000000	2.000000	6.000000
Female Presenters	206.000000	1.194175	0.420412	1.000000	1.000000	1.000000	1.000000	3.000000
Transgender Presenters	3.000000	1.000000	0.000000	1.000000	1.000000	1.000000	1.000000	1.000000
Couple Presenters	437.000000	0.180778	0.385275	0.000000	0.000000	0.000000	0.000000	1.000000
Yearly Revenue	210.000000	596.214286	1601.166969	0.000000	75.000000	170.000000	476.000000	18700.000000
Monthly Sales	210.000000	71.174200	259.176637	0.000000	5.625000	20.000000	59.500000	3500.000000
Gross Margin	117.000000	54.547009	21.149099	3.000000	40.000000	55.000000	69.000000	150.000000
Net Margin	64.000000	21.218750	12.547647	1.000000	10.750000	20.000000	30.000000	55.000000
EBITDA	16.000000	11.531250	12.898926	-20.000000	5.000000	10.500000	18.250000	35.000000
SKUs	27.000000	342.185185	1157.611169	1.000000	9.000000	25.000000	110.000000	6000.000000
Original Ask Amount	441.000000	148.969872	1426.542757	0.000000	50.000000	70.000000	100.000000	30000.000000
Original Offered Equity	441.000000	3.789796	3.693267	0.200000	1.000000	2.500000	5.000000	30.000000
Valuation Requested	441.000000	5357.185214	9191.269411	0.000000	1000.000000	2600.000000	6000.000000	120000.000000
Received Offer	441.000000	0.682540	0.466017	0.000000	0.000000	1.000000	1.000000	1.000000
Accepted Offer	301.000000	0.823920	0.381522	0.000000	1.000000	1.000000	1.000000	1.000000
Total Deal Amount	248.000000	66.083119	43.601659	0.000000	40.000000	58.300000	90.000000	300.000000
Total Deal Equity	248.000000	8.531532	9.637837	0.500000	2.500000	5.000000	10.000000	75.000000
Total Deal Debt	53.000000	46.622642	27.182592	20.000000	25.000000	40.000000	50.000000	150.000000
Debt Interest	37.000000	10.432432	3.586553	0.000000	10.000000	10.000000	12.000000	18.000000
Deal Valuation	247.000000	2299.296411	3535.721814	0.000000	434.500000	1000.000000	2500.000000	25000.000000
Number of Sharks in Deal	248.000000	2.004032	1.143537	1.000000	1.000000	2.000000	3.000000	5.000000
Royalty Deal	14.000000	1.000000	0.000000	1.000000	1.000000	1.000000	1.000000	1.000000
Advisory Shares Equity	3.000000	1.533333	0.950438	0.600000	1.050000	1.500000	2.000000	2.500000
Namita Investment Amount	81.000000	32.911608	20.897607	0.000016	20.000000	28.300000	50.000000	100.000000
Namita Investment Equity	81.000000	4.005909	5.161855	0.200000	1.000000	2.080000	5.000000	25.000000
Namita Debt Amount	14.000000	41.082857	21.412774	12.500000	26.250000	40.500000	50.000000	100.000000
Vineeta Investment Amount	63.000000	31.504167	21.360443	0.002500	17.580000	25.000000	40.000000	100.000000
Vineeta Investment Equity	63.000000	4.292286	4.844934	0.200000	1.000000	2.500000	5.000000	25.000000
Vineeta Debt Amount	11.000000	24.923636	14.011572	12.500000	13.750000	20.000000	30.000000	50.000000
Anupam Investment Amount	70.000000	29.659325	21.892187	0.000000	17.500000	25.000000	40.000000	100.000000
Anupam Investment Equity	70.000000	4.816257	5.385230	0.166000	1.037500	2.500000	6.495000	25.000000
Anupam Debt Amount	7.000000	27.142857	16.100503	12.500000	16.250000	20.000000	37.500000	50.000000
Aman Investment Amount	101.000000	34.612599	24.824278	0.000000	17.500000	30.000000	50.000000	150.000000
Aman Investment Equity	101.000000	3.206758	4.518018	0.166000	1.000000	2.000000	4.000000	40.000000
Aman Debt Amount	14.000000	39.665714	18.308146	16.660000	26.250000	37.500000	47.915000	80.000000
Peyush Investment Amount	87.000000	34.602101	30.408193	0.000000	20.000000	28.000000	45.000000	250.000000
Peyush Investment Equity	87.000000	5.683115	10.959026	0.166000	1.000000	2.000000	5.000000	75.000000
Peyush Debt Amount	11.000000	31.090909	15.332675	10.000000	23.500000	25.000000	40.000000	60.000000
Amit Investment Amount	33.000000	36.193939	27.073697	3.500000	15.000000	25.000000	50.000000	100.000000
Amit Investment Equity	33.000000	4.497170	4.775754	0.330000	1.500000	3.000000	5.000000	20.000000
Amit Debt Amount	6.000000	37.500000	18.641352	10.000000	28.750000	40.000000	43.750000	65.000000
Ashneer Investment Amount	21.000000	25.682381	16.860620	1.000000	15.000000	20.000000	30.000000	70.000000
Ashneer Investment Equity	21.000000	4.440000	5.065662	1.000000	2.000000	3.000000	5.000000	25.000000
Ashneer Debt Amount	2.000000	57.000000	59.396970	15.000000	36.000000	57.000000	78.000000	99.000000
Guest Investment Amount	37.000000	38.423047	37.483512	0.000253	20.000000	30.000000	40.500000	200.000000
Guest Investment Equity	37.000000	3.343108	3.811857	0.200000	1.000000	2.330000	4.000000	17.500000
Guest Debt Amount	6.000000	32.553333	26.097530	12.500000	17.500000	25.000000	32.125000	83.320000
Namita Present	361.000000	1.000000	0.000000	1.000000	1.000000	1.000000	1.000000	1.000000
Vineeta Present	287.000000	1.000000	0.000000	1.000000	1.000000	1.000000	1.000000	1.000000
Anupam Present	390.000000	1.000000	0.000000	1.000000	1.000000	1.000000	1.000000	1.000000
Aman Present	383.000000	1.000000	0.000000	1.000000	1.000000	1.000000	1.000000	1.000000
Peyush Present	295.000000	1.000000	0.000000	1.000000	1.000000	1.000000	1.000000	1.000000
Amit Present	128.000000	1.000000	0.000000	1.000000	1.000000	1.000000	1.000000	1.000000
Ashneer Present	99.000000	1.000000	0.000000	1.000000	1.000000	1.000000	1.000000	1.000000
Guest Present	116.000000	1.155172	0.363640	1.000000	1.000000	1.000000	1.000000	2.000000

	Season Number	Startup Name	Valuation Requested	Deal Valuation
20	1	Kavach	50.00	50.00
22	1	BeyondSnack	2000.00	2000.00
45	1	Cocofit	0.00	0.00
86	1	WattTechnovations	0.00	0.00
171	2	TheSimplySalad	300.00	300.00
185	2	Janitri	4000.00	4000.00
203	2	NeoMotion	10000.00	10000.00
219	2	PadCare	2500.00	2500.00
223	2	Geeani	1000.00	1000.00
240	2	MindPeers	5300.00	5300.00
243	2	DhruvVidyut	0.00	0.00
253	2	TheHealthyBinge	1000.00	1000.00
311	2	Sahayatha	1000.00	1000.00
314	2	NishHair	5000.00	5000.00
334	3	AIKavach/Panoplia	4000.00	4000.00
341	3	WeHear	25000.00	25000.00
354	3	HoneyTwigs	2500.00	2500.00
355	3	Koparo	7000.00	7000.00
363	3	ALittleExtra	800.00	800.00
390	3	Matri	1500.00	1500.00
397	3	Cosmix	10000.00	10000.00
399	3	PolishMePretty	100.00	100.00
405	3	WiseLife	3000.00	3000.00
408	3	AvataarSkincare	7000.00	7000.00
412	3	ModelVerse	250.00	250.00
414	3	TheShellHair	1000.00	1000.00
416	3	MEPACK	70.00	70.00
435	3	Dharaksha	1.25	1.25

	Season Number	Startup Name	Episode Number	Pitch Number	Season Start	Season End	Original Air Date	Episode Title	Anchor	Industry	Business Description	Company Website	Started in	Number of Presenters	Male Presenters	Female Presenters	Transgender Presenters	Couple Presenters	Pitchers Average Age	Pitchers City	Pitchers State	Monthly Sales	Gross Margin	Net Margin	...	Aman Investment Amount	Aman Investment Equity	Aman Debt Amount	Peyush Investment Amount	Peyush Investment Equity	Peyush Debt Amount	Amit Investment Amount	Amit Investment Equity	Amit Debt Amount	Ashneer Investment Amount	Ashneer Investment Equity	Ashneer Debt Amount	Guest Investment Amount	Guest Investment Equity	Guest Debt Amount	Invested Guest Name	All Guest Names	Namita Present	Vineeta Present	Anupam Present	Aman Present	Peyush Present	Amit Present	Ashneer Present	Guest Present
9	1	CosIQ	4	10	20-Dec-21	4-Feb-22	23-Dec-21	Entrepreneurship Ki Wave	Rannvijay Singh	Beauty/Fashion	Intelligent Skincare	https://mycosiq.com/	2021	2	1	1	<NA>	1	Middle	Delhi	Delhi	4.0	75	20	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	1.0	1.0	1.0	NaN	NaN	1.0	NaN
23	1	VivalyfInnovations	8	24	20-Dec-21	4-Feb-22	29-Dec-21	Shark Ko Impress Karne Wale Ideas	Rannvijay Singh	Medical/Health	Easy Life Prickless Diabetes Testing Machine	https://vivalyf.in/	2021	2	1	1	<NA>	0	Young	Hyderabad	Telangana	NaN	<NA>	<NA>	...	NaN	NaN	NaN	28.0	16.66	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	NaN	1.0	1.0	1.0	NaN	1.0	NaN
24	1	MotionBreeze	8	25	20-Dec-21	4-Feb-22	29-Dec-21	Shark Ko Impress Karne Wale Ideas	Rannvijay Singh	Vehicles/Electrical Vehicles	Smart Electric Motorcycle	https://www.motionautomotive.in/	2018	4	4	<NA>	<NA>	0	Middle	Vadodara	Gujarat	NaN	<NA>	<NA>	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	30.0	6.0	NaN	NaN	NaN	NaN	NaN	NaN	1.0	NaN	1.0	1.0	1.0	NaN	1.0	NaN
42	1	GoodGoodPiggy	14	43	20-Dec-21	4-Feb-22	6-Jan-22	Naye Aur Nayab Pitchers	Rannvijay Singh	Technology/Software	Digital Piggy Bank	https://goodgoodpiggy.com/	2021	2	<NA>	2	<NA>	0	Young	Delhi	Delhi	NaN	<NA>	<NA>	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	NaN	1.0	1.0	1.0	NaN	1.0	NaN
77	1	KGAgrotech	24	78	20-Dec-21	4-Feb-22	20-Jan-22	A Decade Of Indian Entrepreneurship	Rannvijay Singh	Agriculture	Agricultural Innovations	https://www.instagram.com/jugaadu_kamlesh/	2022	2	2	<NA>	<NA>	0	Young	Malegaon	Maharashtra	NaN	<NA>	<NA>	...	NaN	NaN	NaN	10.0	40.00	20.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Ghazal Alagh	1.0	1.0	1.0	NaN	1.0	NaN	NaN	1.0
83	1	JulaaAutomation	26	84	20-Dec-21	4-Feb-22	24-Jan-22	Revolutionary Ideas	Rannvijay Singh	Manufacturing	Automatic Cradle	https://www.automaticjulaa.com/	2022	3	3	<NA>	<NA>	0	Middle	Ahmedabad	Gujarat	NaN	<NA>	<NA>	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Ghazal Alagh	1.0	1.0	1.0	NaN	1.0	NaN	NaN	1.0
98	1	Scholify	30	99	20-Dec-21	4-Feb-22	28-Jan-22	Sharks Ki Expertise	Rannvijay Singh	Education	Scholarship Platform	https://scholifyme.com/	2018	1	1	<NA>	<NA>	0	Middle	Bangalore	Karnataka	NaN	<NA>	<NA>	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	1.0	1.0	1.0	NaN	NaN	1.0	NaN
100	1	Sabjikothi	31	101	20-Dec-21	4-Feb-22	31-Jan-22	Entrepreneurship Ki Raah	Rannvijay Singh	Manufacturing	Vegetables Storage SaptKrishi	https://www.saptkrishi.com/	2019	2	1	1	<NA>	0	Young	Bhagalpur	Bihar	NaN	<NA>	<NA>	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	1.0	1.0	1.0	NaN	NaN	1.0	NaN
114	1	On2Cook	34	115	20-Dec-21	4-Feb-22	3-Feb-22	Scaling Ambitions	Rannvijay Singh	Food	Fastest Cooking Device	https://on2cook.com/	2022	1	1	<NA>	<NA>	0	Middle	Ahmedabad	Gujarat	NaN	<NA>	<NA>	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Ghazal Alagh	1.0	1.0	1.0	1.0	1.0	NaN	1.0	1.0
131	1	Scintiglo	0	132	20-Dec-21	4-Feb-22	NaN	Unseen	Rannvijay Singh	Medical/Health	Diagnostic device for microalbuminuria estimation	https://cemd.in/	2021	1	1	<NA>	<NA>	0	Middle	Indore	Madhya Pradesh	NaN	<NA>	<NA>	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
205	2	Sayonara	18	206	2-Jan-23	10-Mar-23	25-Jan-23	Business Ideas With Potential	Rahul Dua	Beauty/Fashion	Petticoat	NaN	<NA>	1	1	<NA>	<NA>	0	Middle	Kolkata	West Bengal	NaN	<NA>	<NA>	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	NaN	1.0	1.0	1.0	1.0	NaN	NaN
206	2	PMV	19	207	2-Jan-23	10-Mar-23	26-Jan-23	Building Brands For India	Rahul Dua	Vehicles/Electrical Vehicles	Personal Mobility Vehicle	https://pmvelectric.com/	2018	1	1	<NA>	<NA>	0	Middle	Mumbai	Maharashtra	NaN	<NA>	<NA>	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	1.0	1.0	1.0	1.0	NaN	NaN	NaN
257	2	CureSee	34	258	2-Jan-23	10-Mar-23	16-Feb-23	Growing Ideas Into Successful Businesses	Rahul Dua	Medical/Health	Artificial Intelligence (AI) based vision therapy	https://curesee.com/	2019	3	3	<NA>	<NA>	0	Middle	Gurgaon	Haryana	264.0	<NA>	<NA>	...	NaN	NaN	NaN	50.0	10.00	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	1.0	1.0	1.0	1.0	NaN	NaN	NaN
264	2	HoloKitab	36	265	2-Jan-23	10-Mar-23	20-Feb-23	Anokhe Pitchers Ke Anokhe Ideas	Rahul Dua	Technology/Software	Augmented Reality content for books	https://www.holokitab.in/	<NA>	2	2	<NA>	<NA>	0	Middle	Jalandhar	Punjab	NaN	<NA>	<NA>	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	1.0	1.0	1.0	1.0	NaN	NaN	NaN
277	2	Hood	40	278	2-Jan-23	10-Mar-23	24-Feb-23	Creating Valuable Businesses	Rahul Dua	Technology/Software	Pseudonymous social network	https://www.hood.live/	2022	3	3	<NA>	<NA>	0	Middle	Gurgaon	Haryana	NaN	<NA>	<NA>	...	30.0	0.27	30.0	30.0	0.27	30.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	NaN	1.0	1.0	1.0	1.0	NaN	NaN
295	2	WaggyZone	44	296	2-Jan-23	10-Mar-23	2-Mar-23	Entrepreneurship Ka Junoon	Rahul Dua	Animal/Pets	Ice Cream Treat for Dogs, Puppies and Cats	https://waggyzone.com/	<NA>	1	<NA>	1	<NA>	0	Middle	Mumbai	Maharashtra	1.0	60	<NA>	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	1.0	1.0	1.0	1.0	NaN	NaN	NaN
350	3	Vecros	10	351	22-Jan-24	NaN	2-Feb-24	Pitching Innovation	Rahul Dua	Technology/Software	Spatial AI drone	https://vecros.com/	2018	2	1	1	<NA>	0	Young	Delhi	Delhi	9.0	<NA>	<NA>	...	20.0	1.00	80.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Deepinder Goyal	1.0	1.0	1.0	1.0	NaN	NaN	NaN	1.0
415	3	Rize	32	416	22-Jan-24	NaN	5-Mar-24	Young Entrepreneurs Make Their Mark	Rahul Dua	Food	Energy Bars	https://rizebar.in/	2023	2	2	<NA>	<NA>	0	Young	Gurgaon	Haryana	NaN	<NA>	<NA>	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Ritesh Aggarwal	1.0	NaN	1.0	1.0	NaN	1.0	NaN	1.0

SharkTank India (Season 1,2,3) Exploratory Data Analysis (EDA) 🦈
¶

⚒️ Importing Required Python Libraries¶

⏳ Check and import dataset¶

💵 Exploratory Data Analysis (EDA)¶

🏦 Season one/two/three of SHARK TANK INDIA was broadcasted in SonyLiv OTT and Sony TV¶

💹 68% companies received offers and 32% startups could not convince Sharks to invest¶

＄ 82% companies accepted offers and 18% startups didn't accept Sharks offer¶

💰 Which shark invested most ?¶

🔥 Ashneer Grover's Investments¶

🎆 Namita Thapar's Investments¶

㊂ Anupam Mittal's Investments¶

🎇 Vineeta Singh's Investments¶

Vineeta's portfolio has 40% Food industry and 23% Beauty/Fashion industry¶

🚀 Aman Gupta's Investments¶

🎾 Peyush Bansal's Investments¶

✳️ Amit Jain's Investments¶

🎆 All Guest's Investments¶

Data set values verification (you can ignore below section)¶

below queries should not return any rows¶

	Season Number	Startup Name	Episode Number	Pitch Number	Season Start	Season End	Original Air Date	Episode Title	Anchor	Industry	Business Description	Company Website	Started in	Number of Presenters	Male Presenters	Female Presenters	Transgender Presenters	Pitchers Average Age	Pitchers City	Pitchers State	Yearly Revenue	Monthly Sales	Gross Margin	Net Margin	EBITDA	Cash Burn	SKUs	Has Patents	Bootstrapped	Original Ask Amount	Original Offered Equity	Valuation Requested	Received Offer	Accepted Offer	Total Deal Amount	Total Deal Equity	Total Deal Debt	Debt Interest	Deal Valuation	Number of Sharks in Deal	Deal Has Conditions	Royalty Deal	Advisory Shares Equity	Namita Investment Amount	Namita Investment Equity	Namita Debt Amount	Vineeta Investment Amount	Vineeta Investment Equity	Vineeta Debt Amount	Anupam Investment Amount	Anupam Investment Equity	Anupam Debt Amount	Aman Investment Amount	Aman Investment Equity	Aman Debt Amount	Peyush Investment Amount	Peyush Investment Equity	Peyush Debt Amount	Amit Investment Amount	Amit Investment Equity	Amit Debt Amount	Ashneer Investment Amount	Ashneer Investment Equity	Ashneer Debt Amount	Guest Investment Amount	Guest Investment Equity	Guest Debt Amount	Invested Guest Name	All Guest Names	Namita Present	Vineeta Present	Anupam Present	Aman Present	Peyush Present	Amit Present	Ashneer Present	Guest Present
136	1	Gizmoswala	0	137	20-Dec-21	4-Feb-22	nan	Unseen	Rannvijay Singh	Entertainment	Sex toys and games	https://www.gizmoswala.com/	2020	3	2	1		Middle	Mumbai	Maharashtra		7.000000	40		nan	nan	nan	nan	nan	75.000000	5.000000	1500.000000	0		nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
227	2	SharmaJiKiAata	25	228	2-Jan-23	10-Mar-23	3-Feb-23	Badhta India	Rahul Dua	Food	Freshly milled atta	https://sharmajikaaata.com/	2019	2	1	1		Middle	Pune	Maharashtra		nan	63	38	nan	nan	nan	nan	nan	40.000000	10.000000	400.000000	1	1	40.000000	20.000000	nan	nan	200.000000	1.000000	nan	nan	nan	nan	nan	nan	nan	nan	nan	40.000000	20.000000	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	1.000000	1.000000	1.000000	1.000000	nan	1.000000	nan	nan
299	2	ZSportsTech	46	300	2-Jan-23	10-Mar-23	6-Mar-23	Different Colours Of Entrepreneurship	Rahul Dua	Sports	Cricket Sport Shop	https://www.zsportstech.com/		2	2			Middle	Mumbai	Maharashtra	31	nan			nan	nan	nan	nan	nan	60.000000	2.000000	3000.000000	0		nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	1.000000	1.000000	1.000000	1.000000	1.000000	nan	nan	nan
7	1	Peeschute	3	8	20-Dec-21	4-Feb-22	22-Dec-21	Aam Aadmi Ke Business Ideas	Rannvijay Singh	Beauty/Fashion	Disposable Urine Bag	https://www.peeschute.com/	2019	1	1			Young	Jalna	Maharashtra	100	nan			nan	nan	2.000000	nan	nan	75.000000	4.000000	1875.000000	1	1	75.000000	6.000000	nan	nan	1250.000000	1.000000	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	75.000000	6.000000	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	1.000000	1.000000	1.000000	1.000000	nan	nan	1.000000	nan
428	3	DesignTemplate	36	429	22-Jan-24	nan	11-Mar-24	Designing Dreams	Rahul Dua	Technology/Software	Online Design Marketplace	https://designtemplate.io/		1	1			Middle	Bid	Maharashtra	160	16.000000			nan	nan	nan	nan	nan	100.000000	2.500000	4000.000000	1	1	100.000000	10.000000	nan	nan	1000.000000	1.000000	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	100.000000	10.000000	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	Ritesh Aggarwal,Radhika Gupta	nan	1.000000	nan	1.000000	1.000000	nan	nan	2.000000
288	2	WTF	43	289	2-Jan-23	10-Mar-23	1-Mar-23	Creating Value Through Ideas	Rahul Dua	Food	Where's The Food	nan		1	1			Middle	Kolkata	West Bengal		nan			nan	nan	nan	nan	nan	75.000000	5.000000	1500.000000	0		nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	1.000000	nan	1.000000	1.000000	1.000000	1.000000	nan	nan
171	2	TheSimplySalad	7	172	2-Jan-23	10-Mar-23	10-Jan-23	Shaandar Businesses	Rahul Dua	Food	Freshly chopped salads	https://simplysalad.com/		2	1	1		Young	Ahmedabad	Gujarat		nan			nan	nan	nan	nan	nan	30.000000	10.000000	300.000000	1	1	30.000000	10.000000	nan	nan	300.000000	2.000000	nan	nan	nan	nan	nan	nan	15.000000	5.000000	nan	nan	nan	nan	15.000000	5.000000	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	1.000000	1.000000	1.000000	1.000000	1.000000	nan	nan	nan
425	3	Namakwali	35	426	22-Jan-24	nan	8-Mar-24	Inspiring Women Entrepreneurs	Rahul Dua	Food	Organic Spices	https://www.namakwali.com/	2018	2	1	1		Middle	nan	Uttarakhand	38	3.000000		17	nan	nan	nan	nan	nan	50.000000	5.000000	1000.000000	1	1	10.000000	5.000000	40.000000	8.000000	200.000000	1.000000	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	10.000000	5.000000	40.000000	nan	nan	nan	nan	nan	nan	nan	nan	1.000000	1.000000	1.000000	1.000000	nan	1.000000	nan	nan
132	1	Glii	0	133	20-Dec-21	4-Feb-22	nan	Unseen	Rannvijay Singh	Services	Dating app for LGBTQ	https://www.glii.in/	2021	4	3		1	Middle	Noida	Uttar Pradesh		nan			nan	nan	nan	nan	nan	40.000000	4.000000	1000.000000	0		nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
386	3	HouseOfBeautyIndia	22	387	22-Jan-24	nan	20-Feb-24	Impressive Numbers and High Stakes	Rahul Dua	Beauty/Fashion	Skin care Products	https://houseofbeautyindia.com/	2021	1		1		Middle	Delhi	Delhi		40.000000			nan	nan	nan	nan	yes	150.000000	5.000000	3000.000000	0		nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	1.000000	1.000000	1.000000	1.000000	nan	1.000000	nan	nan

	Season Number	Startup Name	Total Deal Amount	Total Deal Equity	Total Deal Debt
8	1	NOCD	20.0	15.00	30.0
44	1	PNT	25.0	25.00	25.0
46	1	BambooIndia	50.0	3.50	30.0
56	1	Otua	1.0	1.00	99.0
62	1	TheStatePlate	40.0	3.00	25.0
66	1	Sid07Designs	25.0	75.00	22.0
72	1	NamhyaFoods	50.0	10.00	50.0
77	1	KGAgrotech	10.0	40.00	20.0
120	1	StoreMyGoods	50.0	4.00	50.0
156	2	WatchoutWearables	100.0	10.00	100.0
157	2	SoupX	50.0	18.00	25.0
159	2	Stage	150.0	0.60	150.0
163	2	Brandsdaddy	35.0	5.00	35.0
172	2	AyuSynk	50.0	3.50	50.0
180	2	Freebowler	25.0	7.50	50.0
183	2	DailyDump	30.0	4.00	50.0
196	2	VSMani	19.0	1.00	41.0
201	2	ekatra	20.0	20.00	20.0
204	2	licksters	25.0	5.00	25.0
213	2	Dabble	15.0	10.00	35.0
218	2	HoneyVeda	50.0	20.00	25.0
220	2	SwadeshiBlessings	25.0	5.00	25.0
231	2	BlueTea	50.0	3.00	25.0
248	2	Pabiben	10.0	5.00	40.0
249	2	Homestrap	50.0	7.00	20.0
250	2	uBreathe	50.0	5.00	100.0
252	2	iMumz	10.0	1.00	60.0
254	2	Freakins	50.0	2.50	20.0
277	2	Hood	60.0	0.54	60.0
279	2	GROWiT	50.0	1.00	50.0
282	2	Wol3D	80.0	2.00	70.0
290	2	Aadvik	15.0	1.50	45.0
296	2	NutriCook	50.0	10.00	50.0
297	2	Subhag	20.0	1.00	30.0
306	2	Rubans	100.0	1.00	50.0
309	2	LilGoodness	50.0	1.00	50.0
313	2	maisha	10.0	1.00	65.0
317	2	TAC	81.0	1.00	69.0
318	2	Naara-Aaba	50.0	5.00	25.0

	Season Number	Startup Name	Total Deal Amount	Total Deal Equity
321	3	HonestHome	100.0	3.00
322	3	AdilQadri	100.0	1.00
342	3	Tiggle	50.0	20.00
351	3	GudGum	80.0	10.00
362	3	DecodeAge	100.0	2.25
375	3	YesMadam	150.0	2.00
378	3	PushSports	80.0	4.00
384	3	D'chica	80.0	2.00
385	3	Refit	200.0	1.00
387	3	Artinci	50.0	5.00
397	3	Cosmix	100.0	1.00
401	3	UnclePetersPanCakes	60.0	3.00
410	3	KryzenBiotech	75.0	15.00
433	3	AToddlerThing	40.0	2.00

	Season Number	Startup Name	Original Offered Equity	Total Deal Equity
0	1	BluePineFoods	5.0	16.00
1	1	BoozScooters	15.0	50.00
2	1	HeartUpMySleeves	10.0	30.00
3	1	TagzFoods	1.0	2.75
7	1	Peeschute	4.0	6.00
...	...	...	...	...
424	3	CremeCastle	1.5	2.50
427	3	Deeva	4.0	6.00
428	3	DesignTemplate	2.5	10.00
429	3	Sama	1.0	1.50
440	3	Smotect	1.0	5.00

SharkTank India (Season 1,2,3) Exploratory Data Analysis (EDA) 🦈¶

⚒️ Importing Required Python Libraries¶

⏳ Check and import dataset¶

💵 Exploratory Data Analysis (EDA)¶

🏦 Season one/two/three of SHARK TANK INDIA was broadcasted in SonyLiv OTT and Sony TV¶

💹 68% companies received offers and 32% startups could not convince Sharks to invest¶

＄ 82% companies accepted offers and 18% startups didn't accept Sharks offer¶

💰 Which shark invested most ?¶

🔥 Ashneer Grover's Investments¶

🎆 Namita Thapar's Investments¶

㊂ Anupam Mittal's Investments¶

🎇 Vineeta Singh's Investments¶

Vineeta's portfolio has 40% Food industry and 23% Beauty/Fashion industry¶

🚀 Aman Gupta's Investments¶

🎾 Peyush Bansal's Investments¶

✳️ Amit Jain's Investments¶

🎆 All Guest's Investments¶

Data set values verification (you can ignore below section)¶

below queries should not return any rows¶

SharkTank India (Season 1,2,3) Exploratory Data Analysis (EDA) 🦈
¶